This paper is in the following e-collection/theme issue:

New Methods (944) Digital Health Reviews (1289) Clinical Information and Decision Making (1407) Machine Learning (1465)

Published on in Vol 20, No 10 (2018): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11936, first published .
Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Authors of this article:

Titus Josef Brinker1, 2 Author Orcid Image ;   Achim Hekler1 Author Orcid Image ;   Jochen Sven Utikal3, 4 Author Orcid Image ;   Niels Grabe5 Author Orcid Image ;   Dirk Schadendorf6 Author Orcid Image ;   Joachim Klode6 Author Orcid Image ;   Carola Berking7 Author Orcid Image ;   Theresa Steeb7 Author Orcid Image ;   Alexander H Enk2 Author Orcid Image ;   Christof von Kalle1 Author Orcid Image
Article Authors Cited by (303) Tweetations (24) Metrics

Journals

  1. Triantafyllidis A, Tsanas A. Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature. Journal of Medical Internet Research 2019;21(4):e12286 View
  2. Pacheco A, Krohling R. The impact of patient clinical information on automated skin cancer detection. Computers in Biology and Medicine 2020;116:103545 View
  3. Peine A, Hallawa A, Schöffski O, Dartmann G, Fazlic L, Schmeink A, Marx G, Martin L. A Deep Learning Approach for Managing Medical Consumable Materials in Intensive Care Units via Convolutional Neural Networks: Technical Proof-of-Concept Study. JMIR Medical Informatics 2019;7(4):e14806 View
  4. Hogarty D, Su J, Phan K, Attia M, Hossny M, Nahavandi S, Lenane P, Moloney F, Yazdabadi A. Artificial Intelligence in Dermatology—Where We Are and the Way to the Future: A Review. American Journal of Clinical Dermatology 2020;21(1):41 View
  5. Iglesias-Puzas Á, Boixeda P. Deep learning y DerMATología. Actas Dermo-Sifiliográficas 2020;111(3):192 View
  6. Maron R, Weichenthal M, Utikal J, Hekler A, Berking C, Hauschild A, Enk A, Haferkamp S, Klode J, Schadendorf D, Jansen P, Holland-Letz T, Schilling B, von Kalle C, Fröhling S, Gaiser M, Hartmann D, Gesierich A, Kähler K, Wehkamp U, Karoglan A, Bär C, Brinker T, Schmitt L, Peitsch W, Hoffmann F, Becker J, Drusio C, Lodde G, Sammet S, Sondermann W, Ugurel S, Zader J, Enk A, Salzmann M, Schäfer S, Schäkel K, Winkler J, Wölbing P, Asper H, Bohne A, Brown V, Burba B, Deffaa S, Dietrich C, Dietrich M, Drerup K, Egberts F, Erkens A, Greven S, Harde V, Jost M, Kaeding M, Kosova K, Lischner S, Maagk M, Messinger A, Metzner M, Motamedi R, Rosenthal A, Seidl U, Stemmermann J, Torz K, Velez J, Haiduk J, Alter M, Bergenthal P, Gerlach A, Holtorf C, Kindermann S, Kraas L, Felcht M, Klemke C, Kurzen H, Leibing T, Müller V, Reinhard R, Utikal J, Winter F, Eicher L, Heppt M, Kilian K, Krammer S, Lill D, Niesert A, Oppel E, Sattler E, Senner S, Wallmichrath J, Wolff H, Giner T, Glutsch V, Kerstan A, Presser D, Schrüfer P, Schummer P, Stolze I, Weber J, Drexler K, Mickler M, Stauner C, Thiem A. Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. European Journal of Cancer 2019;119:57 View
  7. Thenault R, Kaulanjan K, Darde T, Rioux-Leclercq N, Bensalah K, Mermier M, Khene Z, Peyronnet B, Shariat S, Pradère B, Mathieu R. The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review. Applied Sciences 2020;10(18):6428 View
  8. Singhal A, Shukla R, Kankar P, Dubey S, Singh S, Pachori R. Comparing the capabilities of transfer learning models to detect skin lesion in humans. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 2020;234(10):1083 View
  9. Adegun A, Viriri S. FCN-Based DenseNet Framework for Automated Detection and Classification of Skin Lesions in Dermoscopy Images. IEEE Access 2020;8:150377 View
  10. Zhou Q, Shi Y, Xu Z, Qu R, Xu G. Classifying Melanoma Skin Lesions Using Convolutional Spiking Neural Networks With Unsupervised STDP Learning Rule. IEEE Access 2020;8:101309 View
  11. Hekler A, Utikal J, Enk A, Solass W, Schmitt M, Klode J, Schadendorf D, Sondermann W, Franklin C, Bestvater F, Flaig M, Krahl D, von Kalle C, Fröhling S, Brinker T. Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. European Journal of Cancer 2019;118:91 View
  12. Han S, Park I, Eun Chang S, Lim W, Kim M, Park G, Chae J, Huh C, Na J. Augmented Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders. Journal of Investigative Dermatology 2020;140(9):1753 View
  13. Mont M, Krebs V, Backstein D, Browne J, Mason J, Taunton M, Callaghan J. Artificial Intelligence: Influencing Our Lives in Joint Arthroplasty. The Journal of Arthroplasty 2019;34(10):2199 View
  14. Brinker T, Hekler A, Enk A, Klode J, Hauschild A, Berking C, Schilling B, Haferkamp S, Schadendorf D, Holland-Letz T, Utikal J, von Kalle C, Ludwig-Peitsch W, Sirokay J, Heinzerling L, Albrecht M, Baratella K, Bischof L, Chorti E, Dith A, Drusio C, Giese N, Gratsias E, Griewank K, Hallasch S, Hanhart Z, Herz S, Hohaus K, Jansen P, Jockenhöfer F, Kanaki T, Knispel S, Leonhard K, Martaki A, Matei L, Matull J, Olischewski A, Petri M, Placke J, Raub S, Salva K, Schlott S, Sody E, Steingrube N, Stoffels I, Ugurel S, Zaremba A, Gebhardt C, Booken N, Christolouka M, Buder-Bakhaya K, Bokor-Billmann T, Enk A, Gholam P, Hänßle H, Salzmann M, Schäfer S, Schäkel K, Schank T, Bohne A, Deffaa S, Drerup K, Egberts F, Erkens A, Ewald B, Falkvoll S, Gerdes S, Harde V, Jost M, Kosova K, Messinger L, Metzner M, Morrison K, Motamedi R, Pinczker A, Rosenthal A, Scheller N, Schwarz T, Stölzl D, Thielking F, Tomaschewski E, Wehkamp U, Weichenthal M, Wiedow O, Bär C, Bender-Säbelkampf S, Horbrügger M, Karoglan A, Kraas L, Faulhaber J, Geraud C, Guo Z, Koch P, Linke M, Maurier N, Müller V, Thomas B, Utikal J, Alamri A, Baczako A, Betke M, Haas C, Hartmann D, Heppt M, Kilian K, Krammer S, Lapczynski N, Mastnik S, Nasifoglu S, Ruini C, Sattler E, Schlaak M, Wolff H, Achatz B, Bergbreiter A, Drexler K, Ettinger M, Halupczok A, Hegemann M, Dinauer V, Maagk M, Mickler M, Philipp B, Wilm A, Wittmann C, Gesierich A, Glutsch V, Kahlert K, Kerstan A, Schrüfer P. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal of Cancer 2019;113:47 View
  15. Heidari A, Pham T, Ifegwu I, Burwell R, Armstrong W, Tjoson T, Whyte S, Giorgioni C, Wang B, Wong B, Chen Z. The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa. Journal of Biophotonics 2020;13(3) View
  16. Hekler A, Utikal J, Enk A, Hauschild A, Weichenthal M, Maron R, Berking C, Haferkamp S, Klode J, Schadendorf D, Schilling B, Holland-Letz T, Izar B, von Kalle C, Fröhling S, Brinker T, Schmitt L, Peitsch W, Hoffmann F, Becker J, Drusio C, Jansen P, Lodde G, Sammet S, Sondermann W, Ugurel S, Zader J, Enk A, Salzmann M, Schäfer S, Schäkel K, Winkler J, Wölbing P, Asper H, Bohne A, Brown V, Burba B, Deffaa S, Dietrich C, Dietrich M, Drerup K, Egberts F, Erkens A, Greven S, Harde V, Jost M, Kaeding M, Kosova K, Lischner S, Maagk M, Messinger A, Metzner M, Motamedi R, Rosenthal A, Seidl U, Stemmermann J, Torz K, Velez J, Haiduk J, Alter M, Bär C, Bergenthal P, Gerlach A, Holtorf C, Karoglan A, Kindermann S, Kraas L, Felcht M, Gaiser M, Klemke C, Kurzen H, Leibing T, Müller V, Reinhard R, Utikal J, Winter F, Eicher L, Hartmann D, Heppt M, Kilian K, Krammer S, Lill D, Niesert A, Oppel E, Sattler E, Senner S, Wallmichrath J, Wolff H, Gesierich A, Giner T, Glutsch V, Kerstan A, Presser D, Schrüfer P, Schummer P, Stolze I, Weber J, Drexler K, Mickler M, Stauner C, Thiem A. Superior skin cancer classification by the combination of human and artificial intelligence. European Journal of Cancer 2019;120:114 View
  17. Brinker T, Hekler A, Enk A, von Kalle C, Huynh D. Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions. PLOS ONE 2019;14(6):e0218713 View
  18. Naeem A, Farooq M, Khelifi A, Abid A. Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities. IEEE Access 2020;8:110575 View
  19. Balaji V, Suganthi S, Rajadevi R, Krishna Kumar V, Saravana Balaji B, Pandiyan S. Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier. Measurement 2020;163:107922 View
  20. Keshtkar K, Keshtkar A, Safarpour A. Classifying colorectal cancer or colorectal polyps in endoscopic setting using convolutional neural network: protocol for a systematic review and meta-analysis. F1000Research 2020;9:1086 View
  21. Lopez-Jimenez F, Attia Z, Arruda-Olson A, Carter R, Chareonthaitawee P, Jouni H, Kapa S, Lerman A, Luong C, Medina-Inojosa J, Noseworthy P, Pellikka P, Redfield M, Roger V, Sandhu G, Senecal C, Friedman P. Artificial Intelligence in Cardiology: Present and Future. Mayo Clinic Proceedings 2020;95(5):1015 View
  22. Kadampur M, Al Riyaee S. Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images. Informatics in Medicine Unlocked 2020;18:100282 View
  23. Blum A, Bosch S, Haenssle H, Fink C, Hofmann-Wellenhof R, Zalaudek I, Kittler H, Tschandl P. Künstliche Intelligenz und Smartphone-Programm-Applikationen (Apps). Der Hautarzt 2020;71(9):691 View
  24. Winkler J, Fink C, Toberer F, Enk A, Deinlein T, Hofmann-Wellenhof R, Thomas L, Lallas A, Blum A, Stolz W, Haenssle H. Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition. JAMA Dermatology 2019;155(10):1135 View
  25. Hekler A, Utikal J, Enk A, Berking C, Klode J, Schadendorf D, Jansen P, Franklin C, Holland-Letz T, Krahl D, von Kalle C, Fröhling S, Brinker T. Pathologist-level classification of histopathological melanoma images with deep neural networks. European Journal of Cancer 2019;115:79 View
  26. de Carvalho T, Noels E, Wakkee M, Udrea A, Nijsten T. Development of Smartphone Apps for Skin Cancer Risk Assessment: Progress and Promise. JMIR Dermatology 2019;2(1):e13376 View
  27. Brinker T, Schlager G, French L, Jutzi T, Kittler H. Computerassistierte Hautkrebsdiagnose. Der Hautarzt 2020;71(9):669 View
  28. Maron R, Utikal J, Hekler A, Hauschild A, Sattler E, Sondermann W, Haferkamp S, Schilling B, Heppt M, Jansen P, Reinholz M, Franklin C, Schmitt L, Hartmann D, Krieghoff-Henning E, Schmitt M, Weichenthal M, von Kalle C, Fröhling S, Brinker T. Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study. Journal of Medical Internet Research 2020;22(9):e18091 View
  29. Cullell-Dalmau M, Otero-Viñas M, Manzo C. Research Techniques Made Simple: Deep Learning for the Classification of Dermatological Images. Journal of Investigative Dermatology 2020;140(3):507 View
  30. Bajwa M, Muta K, Malik M, Siddiqui S, Braun S, Homey B, Dengel A, Ahmed S. Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks. Applied Sciences 2020;10(7):2488 View
  31. Shen J, Zhang C, Jiang B, Chen J, Song J, Liu Z, He Z, Wong S, Fang P, Ming W. Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review. JMIR Medical Informatics 2019;7(3):e10010 View
  32. Canales C, Lee C, Cannesson M. Science Without Conscience Is but the Ruin of the Soul: The Ethics of Big Data and Artificial Intelligence in Perioperative Medicine. Anesthesia & Analgesia 2020;130(5):1234 View
  33. Kutzner H, Jutzi T, Krahl D, Krieghoff‐Henning E, Heppt M, Hekler A, Schmitt M, Maron R, Fröhling S, von Kalle C, Brinker T. Overdiagnosis of melanoma – causes, consequences and solutions. JDDG: Journal der Deutschen Dermatologischen Gesellschaft 2020;18(11):1236 View
  34. Reiter O, Rotemberg V, Kose K, Halpern A. Artificial Intelligence in Skin Cancer. Current Dermatology Reports 2019;8(3):133 View
  35. Nakamura I. Phase diagrams of polymer-containing liquid mixtures with a theory-embedded neural network. New Journal of Physics 2020;22(1):015001 View
  36. Ashfaq M, Minallah N, Ullah Z, Ahmad A, Saeed A, Hafeez A. Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection. Electronics 2019;8(6):672 View
  37. Schaefer J, Lehne M, Schepers J, Prasser F, Thun S. The use of machine learning in rare diseases: a scoping review. Orphanet Journal of Rare Diseases 2020;15(1) View
  38. Yu K, Syed M, Bernardis E, Gelfand J. Machine Learning Applications in the Evaluation and Management of Psoriasis: A Systematic Review. Journal of Psoriasis and Psoriatic Arthritis 2020;5(4):147 View
  39. Iglesias-Puzas Á, Boixeda P. Deep Learning and Mathematical Models in Dermatology. Actas Dermo-Sifiliográficas (English Edition) 2020;111(3):192 View
  40. Dzobo K, Adotey S, Thomford N, Dzobo W. Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine. OMICS: A Journal of Integrative Biology 2020;24(5):247 View
  41. Winkler J, Fink C, Toberer F, Enk A, Hänßle H. Melanomdiagnose mithilfe künstlicher Intelligenz. hautnah dermatologie 2019;35(2):38 View
  42. Mahbod A, Schaefer G, Wang C, Dorffner G, Ecker R, Ellinger I. Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification. Computer Methods and Programs in Biomedicine 2020;193:105475 View
  43. Brinker T, Hekler A, Hauschild A, Berking C, Schilling B, Enk A, Haferkamp S, Karoglan A, von Kalle C, Weichenthal M, Sattler E, Schadendorf D, Gaiser M, Klode J, Utikal J. Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark. European Journal of Cancer 2019;111:30 View
  44. Brinker T, Hekler A, Enk A, Klode J, Hauschild A, Berking C, Schilling B, Haferkamp S, Schadendorf D, Fröhling S, Utikal J, von Kalle C, Ludwig-Peitsch W, Sirokay J, Heinzerling L, Albrecht M, Baratella K, Bischof L, Chorti E, Dith A, Drusio C, Giese N, Gratsias E, Griewank K, Hallasch S, Hanhart Z, Herz S, Hohaus K, Jansen P, Jockenhöfer F, Kanaki T, Knispel S, Leonhard K, Martaki A, Matei L, Matull J, Olischewski A, Petri M, Placke J, Raub S, Salva K, Schlott S, Sody E, Steingrube N, Stoffels I, Ugurel S, Sondermann W, Zaremba A, Gebhardt C, Booken N, Christolouka M, Buder-Bakhaya K, Bokor-Billmann T, Enk A, Gholam P, Hänßle H, Salzmann M, Schäfer S, Schäkel K, Schank T, Bohne A, Deffaa S, Drerup K, Egberts F, Erkens A, Ewald B, Falkvoll S, Gerdes S, Harde V, Jost M, Kosova K, Messinger L, Metzner M, Morrison K, Motamedi R, Pinczker A, Rosenthal A, Scheller N, Schwarz T, Stölzl D, Thielking F, Tomaschewski E, Wehkamp U, Weichenthal M, Wiedow O, Bär C, Bender-Säbelkampf S, Horbrügger M, Karoglan A, Kraas L, Faulhaber J, Geraud C, Guo Z, Koch P, Linke M, Maurier N, Müller V, Thomas B, Utikal J, Alamri A, Baczako A, Betke M, Haas C, Hartmann D, Heppt M, Kilian K, Krammer S, Lapczynski N, Mastnik S, Nasifoglu S, Ruini C, Sattler E, Schlaak M, Wolff H, Achatz B, Bergbreiter A, Drexler K, Ettinger M, Halupczok A, Hegemann M, Dinauer V, Maagk M, Mickler M, Philipp B, Wilm A, Wittmann C, Gesierich A, Glutsch V, Kahlert K, Kerstan A, Schrüfer P. A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal of Cancer 2019;111:148 View
  45. Wang S, Zhang X, Liu J, Tao C, Zhu C, Shu C, Xu T, Jin H. Deep learning-based, computer-aided classifier developed with dermoscopic images shows comparable performance to 164 dermatologists in cutaneous disease diagnosis in the Chinese population. Chinese Medical Journal 2020;133(17):2027 View
  46. Sun Q, Huang C, Chen M, Xu H, Yang Y, Reich A. Skin Lesion Classification Using Additional Patient Information. BioMed Research International 2021;2021:1 View
  47. Cheng C, Chen C, Cheng F, Chen H, Su Y, Yeh C, Chung I, Liao C. A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study. JMIR Medical Informatics 2020;8(11):e19416 View
  48. Hsiao Y, Chiu C, Lu C, Nguyen H, Tseng Y, Hsieh S, Wang H. Identification of Skin Lesions by Using Single-Step Multiframe Detector. Journal of Clinical Medicine 2021;10(1):144 View
  49. Wang J, Chang Y, Tsai K, Wang W, Tsai C, Cheng C, Tsao Y. Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling. Scientific Reports 2020;10(1) View
  50. Höhn J, Hekler A, Krieghoff-Henning E, Kather J, Utikal J, Meier F, Gellrich F, Hauschild A, French L, Schlager J, Ghoreschi K, Wilhelm T, Kutzner H, Heppt M, Haferkamp S, Sondermann W, Schadendorf D, Schilling B, Maron R, Schmitt M, Jutzi T, Fröhling S, Lipka D, Brinker T. Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review. Journal of Medical Internet Research 2021;23(7):e20708 View
  51. Zdolsek G, Chen Y, Bögl H, Wang C, Woisetschläger M, Schilcher J. Deep neural networks with promising diagnostic accuracy for the classification of atypical femoral fractures. Acta Orthopaedica 2021;92(4):394 View
  52. Zhao Z, Wu C, Zhang S, He F, Liu F, Wang B, Huang Y, Shi W, Jian D, Xie H, Yeh C, Li J. A Novel Convolutional Neural Network for the Diagnosis and Classification of Rosacea: Usability Study. JMIR Medical Informatics 2021;9(3):e23415 View
  53. Saba T. Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features. Microscopy Research and Technique 2021;84(6):1272 View
  54. Han S, Moon I, Kim S, Na J, Kim M, Park G, Park I, Kim K, Lim W, Lee J, Chang S, Kittler H. Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study. PLOS Medicine 2020;17(11):e1003381 View
  55. Kutzner H, Jutzi T, Krahl D, Krieghoff‐Henning E, Heppt M, Hekler A, Schmitt M, Maron R, Fröhling S, von Kalle C, Brinker T. Überdiagnose von Melanomen – Ursachen, Konsequenzen und Lösungsansätze. JDDG: Journal der Deutschen Dermatologischen Gesellschaft 2020;18(11):1236 View
  56. Waibel D, Shetab Boushehri S, Marr C. InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification. BMC Bioinformatics 2021;22(1) View
  57. Akkoca Gazioğlu B, Kamaşak M. Effects of objects and image quality on melanoma classification using deep neural networks. Biomedical Signal Processing and Control 2021;67:102530 View
  58. Kaur S, Gupta S, Singh S. Hurricane Damage Detection using Machine Learning and Deep Learning Techniques: A Review. IOP Conference Series: Materials Science and Engineering 2021;1022(1):012035 View
  59. Chagas J, de A. Rodrigues D, Ivo R, Hassan M, de Albuquerque V, Filho P. A new approach for the detection of pneumonia in children using CXR images based on an real-time IoT system. Journal of Real-Time Image Processing 2021;18(4):1099 View
  60. López C, García T, Pérez J, González M, Pose V. Melanoma cutáneo. Medicine - Programa de Formación Médica Continuada Acreditado 2021;13(27):1493 View
  61. Goceri E. Deep learning based classification of facial dermatological disorders. Computers in Biology and Medicine 2021;128:104118 View
  62. Ichim L, Popescu D. Melanoma Detection Using an Objective System Based on Multiple Connected Neural Networks. IEEE Access 2020;8:179189 View
  63. Bispo M, Pierre Júnior M, Apolinário Jr A, dos Santos J, Junior B, Neves F, Crusoé-Rebello I. Computer tomographic differential diagnosis of ameloblastoma and odontogenic keratocyst: classification using a convolutional neural network. Dentomaxillofacial Radiology 2021;50(7):20210002 View
  64. Salamaa W, Aly M. Deep learning design for benign and malignant classification of skin lesions: a new approach. Multimedia Tools and Applications 2021;80(17):26795 View
  65. Zippel C, Bohnet-Joschko S. Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov. International Journal of Environmental Research and Public Health 2021;18(10):5072 View
  66. Dildar M, Akram S, Irfan M, Khan H, Ramzan M, Mahmood A, Alsaiari S, Saeed A, Alraddadi M, Mahnashi M. Skin Cancer Detection: A Review Using Deep Learning Techniques. International Journal of Environmental Research and Public Health 2021;18(10):5479 View
  67. Cano E, Mendoza-Avilés J, Areiza M, Guerra N, Mendoza-Valdés J, Rovetto C. Multi skin lesions classification using fine-tuning and data-augmentation applying NASNet. PeerJ Computer Science 2021;7:e371 View
  68. Winkler J, Fink C, Toberer F, Enk A, Hänßle H. Melanomdiagnose mithilfe künstlicher Intelligenz. Im Fokus Onkologie 2019;22(4):89 View
  69. Mantelakis A, Assael Y, Sorooshian P, Khajuria A. Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery. Plastic and Reconstructive Surgery - Global Open 2021;9(6):e3638 View
  70. Kharisudin I, Hidayati A, Agoestanto A, Mashuri M. Convolutional neural network for classification of skin cancer based on image data using google colab. Journal of Physics: Conference Series 2021;1968(1):012015 View
  71. ERGÜN E, KILIÇ K. Derin Öğrenme ile Artırılmış Görüntü Seti üzerinden Cilt Kanseri Tespiti. Black Sea Journal of Engineering and Science 2021;4(4):192 View
  72. Masuda T, Nakaura T, Funama Y, Oda S, Okimoto T, Sato T, Noda N, Yoshiura T, Baba Y, Arao S, Hiratsuka J, Awai K. Deep learning with convolutional neural network for estimation of the characterisation of coronary plaques: Validation using IB-IVUS. Radiography 2022;28(1):61 View
  73. Mazhar T, Haq I, Ditta A, Mohsan S, Rehman F, Zafar I, Gansau J, Goh L. The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer. Healthcare 2023;11(3):415 View
  74. Fallah K, Ghodsi M. The effectiveness of narrative therapy on sexual function and couple burnout. Revista Portuguesa de Investigação Comportamental e Social 2022;8(1):1 View
  75. Schielein M, Christl J, Sitaru S, Pilz A, Kaczmarczyk R, Biedermann T, Lasser T, Zink A. Outlier detection in dermatology: Performance of different convolutional neural networks for binary classification of inflammatory skin diseases. Journal of the European Academy of Dermatology and Venereology 2023;37(5):1071 View
  76. Du X, Chen Z, Li Q, Yang S, Jiang L, Yang Y, Li Y, Gu Z. Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence. Bio-Design and Manufacturing 2023;6(3):319 View
  77. Asada N, Morita R, Kamiji R, Kuwajima M, Komorisono M, Yamamura T, Ono N, Kanaya S, Yoshikawa S. Evaluation of intercellular lipid lamellae in the stratum corneum by polarized microscopy. Skin Research and Technology 2022;28(3):391 View
  78. Bhimavarapu U, Battineni G. Skin Lesion Analysis for Melanoma Detection Using the Novel Deep Learning Model Fuzzy GC-SCNN. Healthcare 2022;10(5):962 View
  79. Somfai E, Baffy B, Fenech K, Hosszú R, Korózs D, Pólik M, Sárdy M, Lőrincz A. Handling dataset dependence with model ensembles for skin lesion classification from dermoscopic and clinical images. International Journal of Imaging Systems and Technology 2023;33(2):556 View
  80. Rashid J, Ishfaq M, Ali G, Saeed M, Hussain M, Alkhalifah T, Alturise F, Samand N. Skin Cancer Disease Detection Using Transfer Learning Technique. Applied Sciences 2022;12(11):5714 View
  81. Kumar V, Mishra V, Arora M. Deep Learning-Based Classification of Malignant and Benign Cells in Dermatoscopic Images via Transfer Learning Approach. International Journal of Image and Graphics 2022;22(05) View
  82. Sokół S, Pawuś D, Majewski P, Krok M. The Study of the Effectiveness of Advanced Algorithms for Learning Neural Networks Based on FPGA in the Musical Notation Classification Task. Applied Sciences 2022;12(19):9829 View
  83. Chen Y, Lin Y, Xu X, Ding J, Li C, Zeng Y, Liu W, Xie W, Huang J. Classification of lungs infected COVID-19 images based on inception-ResNet. Computer Methods and Programs in Biomedicine 2022;225:107053 View
  84. Nasreen G, Haneef K, Tamoor M, Irshad A. Review: a comparative study of state-of-the-art skin image segmentation techniques with CNN. Multimedia Tools and Applications 2023;82(7):10921 View
  85. Sharma A, Tiwari S, Aggarwal G, Goenka N, Kumar A, Chakrabarti P, Chakrabarti T, Gono R, Leonowicz Z, Jasinski M. Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network. IEEE Access 2022;10:17920 View
  86. Birkner M, Schalk J, von den Driesch P, Schultz E. Computer-Assisted Differential Diagnosis of Pyoderma Gangrenosum and Venous Ulcers with Deep Neural Networks. Journal of Clinical Medicine 2022;11(23):7103 View
  87. Wang X. Deep Learning-based and Machine Learning-based Application in Skin Cancer Image Classification. Journal of Physics: Conference Series 2022;2405(1):012024 View
  88. Melarkode N, Srinivasan K, Qaisar S, Plawiak P. AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions. Cancers 2023;15(4):1183 View
  89. Park G, Lee H, Lee M. Artificial Intelligence-based Healthcare Interventions: A Systematic Review. Korean Journal of Adult Nursing 2021;33(5):427 View
  90. Wu Y, Chen B, Zeng A, Pan D, Wang R, Zhao S. Skin Cancer Classification With Deep Learning: A Systematic Review. Frontiers in Oncology 2022;12 View
  91. Winkler J, Haenssle H. Bilderkennung mittels künstlicher Intelligenz in der Hautkrebsdiagnostik. Die Dermatologie 2022;73(11):838 View
  92. Stiff K, Franklin M, Zhou Y, Madabhushi A, Knackstedt T. Artificial intelligence and melanoma: A comprehensive review of clinical, dermoscopic, and histologic applications. Pigment Cell & Melanoma Research 2022;35(2):203 View
  93. Pasha Syed A, Anbalagan R, Setlur A, Karunakaran C, Shetty J, Kumar J, Niranjan V. Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers. BMC Bioinformatics 2022;23(1) View
  94. Sitaru S, Zink A. Digitalisierung in der Dermatoonkologie: künstliche Intelligenz zur Diagnostik. best practice onkologie 2023;18(1-2):20 View
  95. Vishwajeet Jadhav , Shivani Mane , Pranay Allepally , Neha Sonawane , Prof. Santosh Kale . Effect of Image Enhancement on Early Detection of Skin Cancer. International Journal of Advanced Research in Science, Communication and Technology 2022:11 View
  96. Snyder A, Zhang D, Dreesen S, Baltimore C, Lopez-Garcia D, Akers J, Metts C, Madory J, Chang P, Doan L, Elston D, Valdebran M, Luo F, Forcucci J. Histologic Screening of Malignant Melanoma, Spitz, Dermal and Junctional Melanocytic Nevi Using a Deep Learning Model. The American Journal of Dermatopathology 2022;44(9):650 View
  97. Huang K, Jiang Z, Li Y, Wu Z, Wu X, Zhu W, Chen M, Zhang Y, Zuo K, Li Y, Yu N, Liu S, Huang X, Su J, Yin M, Qian B, Wang X, Chen X, Zhao S. The Classification of Six Common Skin Diseases Based on Xiangya-Derm: Development of a Chinese Database for Artificial Intelligence. Journal of Medical Internet Research 2021;23(9):e26025 View
  98. Mujahid M, Rustam F, Álvarez R, Luis Vidal Mazón J, Díez I, Ashraf I. Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network. Diagnostics 2022;12(5):1280 View
  99. Kshatri S, Singh D. Convolutional Neural Network in Medical Image Analysis: A Review. Archives of Computational Methods in Engineering 2023;30(4):2793 View
  100. Huang H, Hsiao Y, Mukundan A, Tsao Y, Chang W, Wang H. Classification of Skin Cancer Using Novel Hyperspectral Imaging Engineering via YOLOv5. Journal of Clinical Medicine 2023;12(3):1134 View
  101. Pati N, Asish Y, Manoj Kumar K, Prusty M. Oversampled Two-dimensional Deep Learning Model for Septenary Classification of Skin Lesion Disease. National Academy Science Letters 2023;46(2):159 View
  102. Pacheco A, Krohling R. An Attention-Based Mechanism to Combine Images and Metadata in Deep Learning Models Applied to Skin Cancer Classification. IEEE Journal of Biomedical and Health Informatics 2021;25(9):3554 View
  103. Chalkidou A, Shokraneh F, Kijauskaite G, Taylor-Phillips S, Halligan S, Wilkinson L, Glocker B, Garrett P, Denniston A, Mackie A, Seedat F. Recommendations for the development and use of imaging test sets to investigate the test performance of artificial intelligence in health screening. The Lancet Digital Health 2022;4(12):e899 View
  104. Hussain D, Ali Naqvi R, Loh W, Lee J. Deep Learning in DXA Image Segmentation. Computers, Materials & Continua 2021;66(3):2587 View
  105. Popescu D, El-Khatib M, El-Khatib H, Ichim L. New Trends in Melanoma Detection Using Neural Networks: A Systematic Review. Sensors 2022;22(2):496 View
  106. Jones O, Matin R, van der Schaar M, Prathivadi Bhayankaram K, Ranmuthu C, Islam M, Behiyat D, Boscott R, Calanzani N, Emery J, Williams H, Walter F. Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review. The Lancet Digital Health 2022;4(6):e466 View
  107. Palananda A, Kimpan W. Classification of Adulterated Particle Images in Coconut Oil Using Deep Learning Approaches. Applied Sciences 2022;12(2):656 View
  108. Park S, Chien A, Lin B, Li K. FACES: A Deep-Learning-Based Parametric Model to Improve Rosacea Diagnoses. Applied Sciences 2023;13(2):970 View
  109. Saranya S , Vivekanandan S J , Vignesh K , Sai Anand K , Surya Prakash R . Skin Cancer Classification using Tensorflow and Keras. International Journal of Advanced Research in Science, Communication and Technology 2022:916 View
  110. Duong D, Waikel R, Hu P, Tekendo-Ngongang C, Solomon B. Neural network classifiers for images of genetic conditions with cutaneous manifestations. Human Genetics and Genomics Advances 2022;3(1):100053 View
  111. Cassidy B, Kendrick C, Brodzicki A, Jaworek-Korjakowska J, Yap M. Analysis of the ISIC image datasets: Usage, benchmarks and recommendations. Medical Image Analysis 2022;75:102305 View
  112. Mahbod A, Ellinger I. Special Issue on “Advances in Skin Lesion Image Analysis Using Machine Learning Approaches”. Diagnostics 2022;12(8):1928 View
  113. Lembhe A, Motarwar P, Patil R, Elias S. Enhancement in Skin Cancer Detection using Image Super Resolution and Convolutional Neural Network. Procedia Computer Science 2023;218:164 View
  114. Ali R, Hardie R, Narayanan B, Kebede T. IMNets: Deep Learning Using an Incremental Modular Network Synthesis Approach for Medical Imaging Applications. Applied Sciences 2022;12(11):5500 View
  115. Wu M, Wang S, Pan S, Terentis A, Strasswimmer J, Zhu X. Deep learning data augmentation for Raman spectroscopy cancer tissue classification. Scientific Reports 2021;11(1) View
  116. Hasan M, Elahi M, Alam M, Jawad M, Martí R. DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation. Informatics in Medicine Unlocked 2022;28:100819 View
  117. Aishwarya A , Prabhudeva S . Detection of Skin Cancer Based on Image Processing using Machine Learning. International Journal of Advanced Research in Science, Communication and Technology 2022:90 View
  118. Keerthana D, Venugopal V, Nath M, Mishra M. Hybrid convolutional neural networks with SVM classifier for classification of skin cancer. Biomedical Engineering Advances 2023;5:100069 View
  119. Pawuś D, Paszkiel S. BCI Wheelchair Control Using Expert System Classifying EEG Signals Based on Power Spectrum Estimation and Nervous Tics Detection. Applied Sciences 2022;12(20):10385 View
  120. Namburu A, Mohan S, Chakkaravarthy S, Selvaraj P. Skin Cancer Segmentation Based on Triangular Intuitionistic Fuzzy Sets. SN Computer Science 2023;4(3) View
  121. Adla D, Reddy G, Nayak P, Karuna G. A full-resolution convolutional network with a dynamic graph cut algorithm for skin cancer classification and detection. Healthcare Analytics 2023;3:100154 View
  122. Li H, Pan Y, Zhao J, Zhang L. Skin disease diagnosis with deep learning: A review. Neurocomputing 2021;464:364 View
  123. Wang Y, Wang Y, Cai J, Lee T, Miao C, Wang Z. SSD-KD: A self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images. Medical Image Analysis 2023;84:102693 View
  124. Hartmann T, Perron R, Razavi M. Utilization of Nanoparticles, Nanodevices, and Nanotechnology in the Treatment Course of Cutaneous Melanoma. Advanced Therapeutics 2022;5(7) View
  125. Salma W, Eltrass A. Automated deep learning approach for classification of malignant melanoma and benign skin lesions. Multimedia Tools and Applications 2022;81(22):32643 View
  126. Takiddin A, Schneider J, Yang Y, Abd-Alrazaq A, Househ M. Artificial Intelligence for Skin Cancer Detection: Scoping Review. Journal of Medical Internet Research 2021;23(11):e22934 View
  127. Montalbo F. Automating mosquito taxonomy by compressing and enhancing a feature fused EfficientNet with knowledge distillation and a novel residual skip block. MethodsX 2023;10:102072 View
  128. Vidya Lakshmi V, S. Leena Jasmine J. A Hybrid Artificial Intelligence Model for Skin Cancer Diagnosis. Computer Systems Science and Engineering 2021;37(2):233 View
  129. Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. International Journal of Medical Informatics 2022;166:104855 View
  130. Sun M, Kentley J, Mehta P, Dusza S, Halpern A, Rotemberg V. Accuracy of commercially available smartphone applications for the detection of melanoma. British Journal of Dermatology 2022;186(4):744 View
  131. Upadhyay A, Singh G, Mhatre S, Nadar P. Dog Skin Diseases Detection and Identification Using Convolutional Neural Networks. SN Computer Science 2023;4(3) View
  132. Pawuś D, Paszkiel S. Application of EEG Signals Integration to Proprietary Classification Algorithms in the Implementation of Mobile Robot Control with the Use of Motor Imagery Supported by EMG Measurements. Applied Sciences 2022;12(11):5762 View
  133. Alzubaidi L, Duan Y, Al-Dujaili A, Ibraheem I, Alkenani A, Santamaría J, Fadhel M, Al-Shamma O, Zhang J. Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study. PeerJ Computer Science 2021;7:e715 View
  134. Patel M. Multi Class Skin Diseases Classification Based On Dermoscopic Skin Images Using Deep Learning. International Journal of Next-Generation Computing 2022 View
  135. Gottumukkala V, Kumaran N, Sekhar V. BLSNet: Skin lesion detection and classification using broad learning system with incremental learning algorithm. Expert Systems 2022;39(9) View
  136. Raju D, Shanmugasundaram H, Sasikumar R. Fuzzy segmentation and black widow–based optimal SVM for skin disease classification. Medical & Biological Engineering & Computing 2021;59(10):2019 View
  137. Ajmal M, Khan M, Akram T, Alqahtani A, Alhaisoni M, Armghan A, Althubiti S, Alenezi F. BF2SkNet: best deep learning features fusion-assisted framework for multiclass skin lesion classification. Neural Computing and Applications 2023;35(30):22115 View
  138. Zafar M, Sharif M, Sharif M, Kadry S, Bukhari S, Rauf H. Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey. Life 2023;13(1):146 View
  139. Tian Y, Sun S, Qi Z, Liu Y, Wang Z. Non-tumorous facial pigmentation classification based on multi-view convolutional neural network with attention mechanism. Neurocomputing 2022;483:370 View
  140. Shehzad K, Zhenhua T, Shoukat S, Saeed A, Ahmad I, Sarwar Bhatti S, Chelloug S. A Deep-Ensemble-Learning-Based Approach for Skin Cancer Diagnosis. Electronics 2023;12(6):1342 View
  141. Priyadharshini N, N. S, Hemalatha B, Sureshkumar C. A novel hybrid Extreme Learning Machine and Teaching–Learning-Based​ Optimization algorithm for skin cancer detection. Healthcare Analytics 2023;3:100161 View
  142. Liu S, Chen M. Wire Rope Defect Recognition Method Based on MFL Signal Analysis and 1D-CNNs. Sensors 2023;23(7):3366 View
  143. Naguib S, Hamza H, Hosny K, Saleh M, Kassem M. Classification of Cervical Spine Fracture and Dislocation Using Refined Pre-Trained Deep Model and Saliency Map. Diagnostics 2023;13(7):1273 View
  144. To H, Nguyen H, Le H, Le H, Quan T. MetaAttention model: a new approach for skin lesion diagnosis using AB features and attention mechanism. Biomedical Physics & Engineering Express 2023;9(4):045008 View
  145. Xu D, Zhou B, Zhang J, Li C, Guan C, Liu Y, Li L, Li H, Cui L, Xu L, Liu H, Zhen L, Xu Y. Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG. Renal Failure 2023;45(1) View
  146. Juan C, Su Y, Wu C, Yang C, Hsu C, Hung C, Chen Y. Deep convolutional neural network with fusion strategy for skin cancer recognition: model development and validation. Scientific Reports 2023;13(1) View
  147. Kuo K, Talley P, Chang C. The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis. BMC Medical Informatics and Decision Making 2023;23(1) View
  148. Liu Q, Zhang J, Bai Y. Mapping the landscape of artificial intelligence in skin cancer research: a bibliometric analysis. Frontiers in Oncology 2023;13 View
  149. Kumar R, Sood P, Nirala R, Ade R, Sonaji A. Uses of AI in Field of Radiology- What is State of Doctor & Pateints Communication in Different Disease for Diagnosis Purpose. Journal for Research in Applied Sciences and Biotechnology 2023;2(5):51 View
  150. Shah A, Shah M, Pandya A, Sushra R, Sushra R, Mehta M, Patel K, Patel K. A comprehensive study on skin cancer detection using artificial neural network (ANN) and convolutional neural network (CNN). Clinical eHealth 2023;6:76 View
  151. Zhang L, Xu R, Zhao J. Learning technology for detection and grading of cancer tissue using tumour ultrasound images1. Journal of X-Ray Science and Technology 2024;32(1):157 View
  152. Shafi N, Costantine J, Kanj R, Tawk Y, Ramadan A, Kurban M, Rahal J, Eid A. A Portable Non-Invasive Electromagnetic Lesion-Optimized Sensing Device for the Diagnosis of Skin Cancer (SkanMD). IEEE Transactions on Biomedical Circuits and Systems 2023;17(3):558 View
  153. Hussain M, Fiza M, Khalil A, Siyal A, Dharejo F, Hyder W, Guzzo A, Krichen M, Fortino G. Transfer learning-based quantized deep learning models for nail melanoma classification. Neural Computing and Applications 2023;35(30):22163 View
  154. DİMİLİLER K, SEKEROGLU B. Skin Lesion Classification Using CNN-based Transfer Learning Model. Gazi University Journal of Science 2023;36(2):660 View
  155. Ogundokun R, Li A, Babatunde R, Umezuruike C, Sadiku P, Abdulahi A, Babatunde A. Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models. Bioengineering 2023;10(8):979 View
  156. AlSuwaidan L. Deep Learning Based Classification of Dermatological Disorders. Biomedical Engineering and Computational Biology 2023;14 View
  157. Singareddy S, SN V, Jaramillo A, Yasir M, Iyer N, Hussein S, Nath T. Artificial Intelligence and Its Role in the Management of Chronic Medical Conditions: A Systematic Review. Cureus 2023 View
  158. Ajuwon B, Awotundun O, Richardson A, Roper K, Sheel M, Rahman N, Salako A, Lidbury B. Machine learning prediction models for clinical management of blood-borne viral infections: a systematic review of current applications and future impact. International Journal of Medical Informatics 2023;179:105244 View
  159. Rostamzadeh-Renani R, Jasim D, Baghoolizadeh M, Rostamzadeh-Renani M, Andani H, Salahshour S, Baghaei S. Multi-objective optimization of rheological behavior of nanofluids containing CuO nanoparticles by NSGA II, MOPSO, and MOGWO evolutionary algorithms and group method of data handling artificial neural networks. Materials Today Communications 2024;38:107709 View
  160. Fernandes J, Teles A, Fernandes T, Lima L, Balhara S, Gupta N, Teixeira S. Artificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review. Journal of Clinical Medicine 2023;13(1):180 View
  161. Lv J, Zhang R, Gu Q, Hemayet Uddin M, Jiang X, Qi J, Si G, Ou Q. Metasurfaces and their intelligent advances. Materials & Design 2024;237:112610 View
  162. Shakeel C, Khan S. Machine learning (ML) techniques as effective methods for evaluating hair and skin assessments: A systematic review. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 2024;238(2):132 View
  163. Hossain M, Hossain M, Arefin M, Akhtar F, Blake J. Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble. Diagnostics 2023;14(1):89 View
  164. Keskenler M, Çelik E, Dal D. A New Multi-Layer Machine Learning (MLML) Architecture for Non-invasive Skin Cancer Diagnosis on Dermoscopic Images. Journal of Electrical Engineering & Technology 2024;19(4):2739 View
  165. Benzyane M, Azrour M, Zeroual I, Agoujil S. Investigating the Influence of Convolutional Operations on LSTM Networks in Video Classification. Data and Metadata 2023;2:152 View
  166. M T, Koti M, B.A N, V G, K.P S, Mathivanan S, Dalu G. Lung cancer diagnosis based on weighted convolutional neural network using gene data expression. Scientific Reports 2024;14(1) View
  167. Rostamzadeh-Renani M, Baghoolizadeh M, Sajadi S, Rostamzadeh-Renani R, Azarkhavarani N, Salahshour S, Toghraie D. A multi-objective and CFD based optimization of roof-flap geometry and position for simultaneous drag and lift reduction. Propulsion and Power Research 2024 View
  168. Maneesha A, Anusha K, Kalyani B, Satyanarayana K, Bobba P, Perveen A, Debnath S. Adaptive dermascopy application using machine learning. MATEC Web of Conferences 2024;392:01159 View
  169. Mukhlif Y, Ramaha N, Hameed A, Salman M, Yon D, Fitriyani N, Syafrudin M, Lee S. Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review. Mathematics 2024;12(7):1049 View
  170. Ennaji A, Sabri M, Aarab A. Ensemble learning with weighted voting classifier for melanoma diagnosis. Multimedia Tools and Applications 2024 View
  171. Remya S, Anjali T, Sugumaran V. A Novel Transfer Learning Framework for Multimodal Skin Lesion Analysis. IEEE Access 2024;12:50738 View
  172. Uranbey Ö, Özbey F, Kaygısız Ö, Ayrancı F. Assessing ChatGPT's Diagnostic Accuracy and Therapeutic Strategies in Oral Pathologies: A Cross-Sectional Study. Cureus 2024 View
  173. Zaway L, Ben Amor N, Ktari J, Jallouli M, Chrifi Alaoui L, Delahoche L. Optimization of Wheelchair Control via Multi-Modal Integration: Combining Webcam and EEG. Future Internet 2024;16(5):158 View
  174. Liu P, Qian W, Li H, Cao J. A relationship-aware mutual learning method for lightweight skin lesion classification. Digital Communications and Networks 2024 View
  175. Heo E, Park C, Chang K, Shim J, Seo S, Kim D, Cho S, Kim C, Lee N, Lee S. Analytic validation of convolutional neural network-based classification of pigmented skin lesions (PSLs) using unseen PSL hyperspectral data for clinical applications. Journal of the Korean Physical Society 2024;84(11):889 View
  176. Jin W, Basem A, Baghoolizadeh M, Kamoon S, Al-Yasiri M, Salahshour S, Hekmatifar M. Regression modeling and multi-objective optimization of rheological behavior of non-Newtonian hybrid antifreeze: Using different neural networks and evolutionary algorithms. International Communications in Heat and Mass Transfer 2024;155:107578 View
  177. Salinas M, Sepúlveda J, Hidalgo L, Peirano D, Morel M, Uribe P, Rotemberg V, Briones J, Mery D, Navarrete-Dechent C. A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis. npj Digital Medicine 2024;7(1) View
  178. Kumar A, Kumar M, Bhardwaj V, Kumar S, Selvarajan S. A novel skin cancer detection model using modified finch deep CNN classifier model. Scientific Reports 2024;14(1) View
  179. Shukla M, Tripathi B, Dwivedi T, Tripathi A, Chaurasia B. A hybrid CNN with transfer learning for skin cancer disease detection. Medical & Biological Engineering & Computing 2024;62(10):3057 View
  180. Cataldo A, Cino L, Distante C, Maietta G, Masciullo A, Mazzeo P, Schiavoni R. Integrating microwave reflectometry and deep learning imaging for in-vivo skin cancer diagnostics. Measurement 2024;235:114911 View
  181. Spiesman B, Gratton C, Gratton E, Hines H, Cini A. Deep learning for identifying bee species from images of wings and pinned specimens. PLOS ONE 2024;19(5):e0303383 View
  182. Escalé-Besa A, Vidal-Alaball J, Miró Catalina Q, Gracia V, Marin-Gomez F, Fuster-Casanovas A. The Use of Artificial Intelligence for Skin Disease Diagnosis in Primary Care Settings: A Systematic Review. Healthcare 2024;12(12):1192 View
  183. Nielsen J, Kristiansen I, Thapa S. Increasing melanoma incidence with unchanged mortality: more sunshine, better treatment, increased diagnostic activity, overdiagnosis or lowered diagnostic threshold?. British Journal of Dermatology 2024;191(3):365 View
  184. Pradhan J, Singh A, Kumar A, Khan M. Skin lesion classification using modified deep and multi-directional invariant handcrafted features. Journal of Network and Computer Applications 2024;231:103949 View
  185. Natha P, RajaRajeswari P. Advancing Skin Cancer Prediction Using Ensemble Models. Computers 2024;13(7):157 View
  186. Raman R, Pattnaik D, Hughes L, Nedungadi P. Unveiling the dynamics of AI applications: A review of reviews using scientometrics and BERTopic modeling. Journal of Innovation & Knowledge 2024;9(3):100517 View
  187. Ye Z, Zhang D, Zhao Y, Chen M, Wang H, Seery S, Qu Y, Xue P, Jiang Y. Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis. Artificial Intelligence in Medicine 2024;155:102934 View
  188. Winkler J, Kommoss K, Vollmer A, Blum A, Stolz W, Kränke T, Hofmann-Wellenhof R, Enk A, Toberer F, Haenssle H. Computerizing the first step of the two-step algorithm in dermoscopy: A convolutional neural network for differentiating melanocytic from non-melanocytic skin lesions. European Journal of Cancer 2024;210:114297 View
  189. Philipp W, Yashwanthika R, Sikha O, Benitez R. Generation of Rule-Based Explanations of CNN Classifiers Using Regional Features. Neural Processing Letters 2024;56(5) View
  190. Soni U, Gupta J, Singh K, Khandelwal G. Screening and Analysis of Skin Cancer Treatment Using Biocomponents of Plants Using Backpropagation Neural Networks: A Comprehensive Review. Current Cancer Therapy Reviews 2024;20(6):555 View
  191. Jütte L, González-Villà S, Quintana J, Steven M, Garcia R, Roth B. Integrating generative AI with ABCDE rule analysis for enhanced skin cancer diagnosis, dermatologist training and patient education. Frontiers in Medicine 2024;11 View
  192. Li Q, Zhang Y, Wang L, Zhang H, Wang P, Gu M, Xu S. Lightweight skin cancer detection IP hardware implementation using cycle expansion and optimal computation arrays methods. Computers in Biology and Medicine 2024;183:109258 View
  193. Mavaddati S. Skin cancer classification based on a hybrid deep model and long short-term memory. Biomedical Signal Processing and Control 2025;100:107109 View

Books/Policy Documents

  1. Singh N, Kaur A. Interdisciplinary Approaches to Altering Neurodevelopmental Disorders. View
  2. Martorell-Marugán J, Tabik S, Benhammou Y, del Val C, Zwir I, Herrera F, Carmona-Sáez P. Computational Biology. View
  3. El-khatib H, Popescu D, Ichim L. Advances in Computational Intelligence. View
  4. Young K, Booth G, Simpson B, Dutton R, Shrapnel S. Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support. View
  5. Sivasubramanian K, Xing L. LED-Based Photoacoustic Imaging. View
  6. Aldwgeri A, Abubacker N. Advances in Visual Informatics. View
  7. Janoria H, Minj J, Patre P. Intelligent Data Communication Technologies and Internet of Things. View
  8. Iglesias-Puzas Á, Boixeda P. Photography in Clinical Medicine. View
  9. Dwivedi P, Sandhu S, Zeeshan M, Sarkar P. Soft Computing Techniques and Applications. View
  10. Mehra A, Bhati A, Kumar A, Malhotra R. Emerging Technologies in Data Mining and Information Security. View
  11. Dutta A, Kamrul Hasan M, Ahmad M. Proceedings of International Joint Conference on Advances in Computational Intelligence. View
  12. Gao Y, Tang Z, Zhou M, Metaxas D. Information Processing in Medical Imaging. View
  13. Das T, Kumar V, Prakash A, Lynn A. Skin Cancer: Pathogenesis and Diagnosis. View
  14. Nunnari F, Kadir M, Sonntag D. Machine Learning and Knowledge Extraction. View
  15. Jaworek-Korjakowska J, Yap M, Bhattacharjee D, Kleczek P, Brodzicki A, Gorgon M. State of the Art in Neural Networks and Their Applications. View
  16. Barbhuiya R, Ahmad N, Akram W. Computational Intelligence in Oncology. View
  17. Biswal P, Saha M, Jaiswal N, Rout M. The New Advanced Society. View
  18. Panigrahi A, Bhutia S, Sahu B, Galety M, Mohanty S. Disruptive Technologies for Big Data and Cloud Applications. View
  19. Orrin E, Cassidy P, Kulkarni R, Berry E, Leachman S. Melanoma in Clinical Practice. View
  20. Chauhan A, Hasija Y. Proceedings of Emerging Trends and Technologies on Intelligent Systems. View
  21. Kortała M, Jaworska T, Ganzha M, Paprzycki M. Big-Data-Analytics in Astronomy, Science, and Engineering. View
  22. Keerthana D, Nath M. Proceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences. View
  23. Agilandeeswari L, Bansal A, Sasank P, Yasasvi K. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). View
  24. Sai Venkatesh C, Meriga C, Geethika M, Lakshmi Gayatri T, Aruna V. High Performance Computing and Networking. View
  25. Pundhir A, Agarwal A, Dadhich S, Raman B. Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support, and Topological Data Analysis for Biomedical Imaging. View
  26. Pike A, Benkli B, Gilani S, Hirani S. Substance Use and Addiction Research. View
  27. Dhiman A, Chauhan N. Proceedings of the International Health Informatics Conference. View
  28. Karpagam G, Keerthna M, Naresh K, Sairam Vaidya M, Karthikeyan T, Mohideen S. Artificial Intelligence for Sustainable Applications. View
  29. Singh C, Nischitha , Shetty S, Bekal A, Bhat S, Badiger M. Advances in VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems. View
  30. Deepa Nivethika S. , Srinivasan D, SenthilPandian M. , Paulraj P, Ashokkumar N, Hariharan K. , Maneesh Vijay V. I. , Raghuram T. . Neuromorphic Computing Systems for Industry 4.0. View
  31. Al-Qaisi A, George L. Proceedings of Eighth International Congress on Information and Communication Technology. View
  32. Goindi S, Thakur K, Kapoor D. Advances in IoT and Security with Computational Intelligence. View
  33. Surówka G. Artificial Intelligence and Soft Computing. View
  34. Shashidhara G, Agarwal R, Suryavamshi J. Intelligent Systems and Machine Learning. View
  35. Obaid A, Suman Rajest S, Silvia Priscila S, Shynu T, Ettyem S. Proceedings of Data Analytics and Management. View
  36. Benzyane M, Azrour M, Zeroual I, Agoujil S. Artificial Intelligence, Data Science and Applications. View
  37. Gupta A, Kuresan H, Talha A, Abhinav P, Dhanalakshmi S. Human-Centric Smart Computing. View
  38. King I, Meng H, Lam T. Artificial Intelligence in Medicine. View
  39. Siddarth C, Poreddy A, Kokil P. Computer Vision and Image Processing. View
  40. Dhage S, Chawan H, Hoskote A, Dabholkar A, Deshmukh V. Navigating the Technological Tide: The Evolution and Challenges of Business Model Innovation. View
  41. Borghesi A, Calegari R. AI for Health Equity and Fairness. View
  42. Haghshenas F, Krzyżak A, Osowski S. Artificial Neural Networks in Pattern Recognition. View
  43. Mahmud T, Barua K, Chakma K, Chakma R, Sharmen N, Kaiser M, Hossain M, Hossain M, Andersson K. Proceedings of Trends in Electronics and Health Informatics. View