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

New Methods (901) Digital Health Reviews (1174) Clinical Information and Decision Making (1314) Machine Learning (1370)

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 (283) Tweetations (24) Metrics

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  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
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  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
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  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
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  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
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  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
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  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
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  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
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  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
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  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
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  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
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  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
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  156. AlSuwaidan L. Deep Learning Based Classification of Dermatological Disorders. Biomedical Engineering and Computational Biology 2023;14 View
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  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
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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