Published on in Vol 21, No 7 (2019): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14464, first published .
Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review

Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review

Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review

Journals

  1. Dalwinder S, Birmohan S, Manpreet K. Simultaneous feature weighting and parameter determination of Neural Networks using Ant Lion Optimization for the classification of breast cancer. Biocybernetics and Biomedical Engineering 2020;40(1):337 View
  2. Yang Q, Guo Y, Ou X, Wang J, Hu C. Automatic T Staging Using Weakly Supervised Deep Learning for Nasopharyngeal Carcinoma on MR Images. Journal of Magnetic Resonance Imaging 2020;52(4):1074 View
  3. Kriti , Virmani J, Agarwal R. Deep feature extraction and classification of breast ultrasound images. Multimedia Tools and Applications 2020;79(37-38):27257 View
  4. Zhang C, Zhao J, Niu J, Li D, Stoean R. New convolutional neural network model for screening and diagnosis of mammograms. PLOS ONE 2020;15(8):e0237674 View
  5. Costa E, Ferreira-Gonçalves T, Chasqueira G, Cabrita A, Figueiredo I, Reis C. Experimental Models as Refined Translational Tools for Breast Cancer Research. Scientia Pharmaceutica 2020;88(3):32 View
  6. El-Nabawy A, El-Bendary N, Belal N. A feature-fusion framework of clinical, genomics, and histopathological data for METABRIC breast cancer subtype classification. Applied Soft Computing 2020;91:106238 View
  7. Chang R, Chuang S, Hsu C, Yen A, Wu W, Chen S, Fann J, Tabar L, Smith R, Duffy S, Chiu S, Chen H. Precision Science on Incidence and Progression of Early-Detected Small Breast Invasive Cancers by Mammographic Features. Cancers 2020;12(7):1855 View
  8. Hassan S, Sayed M, Abdalla M, Rashwan M. Breast cancer masses classification using deep convolutional neural networks and transfer learning. Multimedia Tools and Applications 2020;79(41-42):30735 View
  9. Geweid G, Abdallah M. A Novel Approach for Breast Cancer Investigation and Recognition Using M-Level Set-Based Optimization Functions. IEEE Access 2019;7:136343 View
  10. Papandrianos N, Papageorgiou E, Anagnostis A, Feleki A. A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans. Applied Sciences 2020;10(3):997 View
  11. 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
  12. Sugimori H, Kawakami M. Automatic Detection of a Standard Line for Brain Magnetic Resonance Imaging Using Deep Learning. Applied Sciences 2019;9(18):3849 View
  13. Adachi M, Fujioka T, Mori M, Kubota K, Kikuchi Y, Xiaotong W, Oyama J, Kimura K, Oda G, Nakagawa T, Uetake H, Tateishi U. Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images. Diagnostics 2020;10(5):330 View
  14. Wang X, Liang G, Zhang Y, Blanton H, Bessinger Z, Jacobs N. Inconsistent Performance of Deep Learning Models on Mammogram Classification. Journal of the American College of Radiology 2020;17(6):796 View
  15. Vijayan D, Lavanya R. Ensemble of density-specific experts for mass characterization in mammograms. Signal, Image and Video Processing 2021;15(5):1011 View
  16. Suh Y, Jung J, Cho B. Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning. Journal of Personalized Medicine 2020;10(4):211 View
  17. Jiménez-Gaona Y, Rodríguez-Álvarez M, Lakshminarayanan V. Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review. Applied Sciences 2020;10(22):8298 View
  18. 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
  19. Ramadan S, Fantacci M. Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms. Computational and Mathematical Methods in Medicine 2020;2020:1 View
  20. Tariq M, Iqbal S, Ayesha H, Abbas I, Ahmad K, Niazi M. Medical image based breast cancer diagnosis: State of the art and future directions. Expert Systems with Applications 2021;167:114095 View
  21. Owais M, Arsalan M, Mahmood T, Kim Y, Park K. Comprehensive Computer-Aided Decision Support Framework to Diagnose Tuberculosis From Chest X-Ray Images: Data Mining Study. JMIR Medical Informatics 2020;8(12):e21790 View
  22. Sugimori H, Hamaguchi H, Fujiwara T, Ishizaka K. Classification of type of brain magnetic resonance images with deep learning technique. Magnetic Resonance Imaging 2021;77:180 View
  23. Li L, Du B, Liu H, Chen C. Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives. Frontiers in Oncology 2021;10 View
  24. Yin J, Ngiam K, Teo H. Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. Journal of Medical Internet Research 2021;23(4):e25759 View
  25. Puttagunta M, Ravi S. Medical image analysis based on deep learning approach. Multimedia Tools and Applications 2021;80(16):24365 View
  26. Yu H, Sun H, Li J, Shi L, Bao N, Li H, Qian W, Zhou S. Effective diagnostic model construction based on discriminative breast ultrasound image regions using deep feature extraction. Medical Physics 2021;48(6):2920 View
  27. Li J, Zhou Z, Dong J, Fu Y, Li Y, Luan Z, Peng X, Baltzer P. Predicting breast cancer 5-year survival using machine learning: A systematic review. PLOS ONE 2021;16(4):e0250370 View
  28. Yakar M, Etiz D. Artificial intelligence in rectal cancer. Artificial Intelligence in Gastroenterology 2021;2(2):10 View
  29. 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
  30. Qu J, Zhao X, Chen P, Wang Z, Liu Z, Yang B, Li H. Deep learning on digital mammography for expert-level diagnosis accuracy in breast cancer detection. Multimedia Systems 2022;28(4):1263 View
  31. Michael E, Ma H, Li H, Kulwa F, Li J, Harrison P. Breast Cancer Segmentation Methods: Current Status and Future Potentials. BioMed Research International 2021;2021:1 View
  32. Keyvanpour M, Barani Shirzad M, Mahdikhani L. WARM: a new breast masses classification method by weighting association rule mining. Signal, Image and Video Processing 2022;16(2):481 View
  33. Sivasubramanian M, Lo L. Assessment of Nanoparticle-Mediated Tumor Oxygen Modulation by Photoacoustic Imaging. Biosensors 2022;12(5):336 View
  34. Aldhyani T, Nair R, Alzain E, Alkahtani H, Koundal D. Deep Learning Model for the Detection of Real Time Breast Cancer Images Using Improved Dilation-Based Method. Diagnostics 2022;12(10):2505 View
  35. Rahman H, Naik Bukht T, Ahmad R, Almadhor A, Javed A, Ben Aoun N. Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network. Computational Intelligence and Neuroscience 2023;2023:1 View
  36. Ayana G, Dese K, Dereje Y, Kebede Y, Barki H, Amdissa D, Husen N, Mulugeta F, Habtamu B, Choe S. Vision-Transformer-Based Transfer Learning for Mammogram Classification. Diagnostics 2023;13(2):178 View
  37. Liu Y, Wang X, Li J, Hao L, Zhao T, Zou H, Xu D, Khalaf O. Deep Learning Technology in Pathological Image Analysis of Breast Tissue. Journal of Healthcare Engineering 2021;2021:1 View
  38. Baccouche A, Garcia-Zapirain B, Zheng Y, Elmaghraby A. Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques. Computer Methods and Programs in Biomedicine 2022;221:106884 View
  39. Baghdadi N, Malki A, Magdy Balaha H, AbdulAzeem Y, Badawy M, Elhosseini M. Classification of breast cancer using a manta-ray foraging optimized transfer learning framework. PeerJ Computer Science 2022;8:e1054 View
  40. Ayana G, Park J, Choe S. Patchless Multi-Stage Transfer Learning for Improved Mammographic Breast Mass Classification. Cancers 2022;14(5):1280 View
  41. Wang D, Liu M, Zhuang Z, Wu S, Zhou P, Chen X, Zhu H, Liu H, Zhang L. Radiomics Analysis on Digital Breast Tomosynthesis: Preoperative Evaluation of Lymphovascular Invasion Status in Invasive Breast Cancer. Academic Radiology 2022;29(12):1773 View
  42. Yoshida R, Yamauchi T, Akashi-Tanaka S, Matsuyanagi M, Taruno K, Sawada T, Kokaze A, Nakamura S. Optimal Breast Density Characterization Using a Three-Dimensional Automated Breast Densitometry System. Current Oncology 2021;28(6):5384 View
  43. de la Luz Escobar M, De la Rosa J, Galván-Tejada C, Galvan-Tejada J, Gamboa-Rosales H, de la Rosa Gomez D, Luna-García H, Celaya-Padilla J. Breast Cancer Detection Using Automated Segmentation and Genetic Algorithms. Diagnostics 2022;12(12):3099 View
  44. Zebari D, Ibrahim D, Zeebaree D, Haron H, Salih M, Damaševičius R, Mohammed M. Systematic Review of Computing Approaches for Breast Cancer Detection Based Computer Aided Diagnosis Using Mammogram Images. Applied Artificial Intelligence 2021;35(15):2157 View
  45. Zahoor S, Shoaib U, Lali I. Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm. Diagnostics 2022;12(2):557 View
  46. Naik M, Panda R, Abraham A. An entropy minimization based multilevel colour thresholding technique for analysis of breast thermograms using equilibrium slime mould algorithm. Applied Soft Computing 2021;113:107955 View
  47. Karthik R, Menaka R, Siddharth M. Classification of breast cancer from histopathology images using an ensemble of deep multiscale networks. Biocybernetics and Biomedical Engineering 2022;42(3):963 View
  48. Oza P, Sharma P, Patel S, Kumar P. Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey. Neural Computing and Applications 2022;34(3):1815 View
  49. Baccouche A, Garcia-Zapirain B, Castillo Olea C, S. Elmaghraby A. Breast Lesions Detection and Classification via YOLO-Based Fusion Models. Computers, Materials & Continua 2021;69(1):1407 View
  50. Loizidou K, Skouroumouni G, Nikolaou C, Pitris C. Automatic Breast Mass Segmentation and Classification Using Subtraction of Temporally Sequential Digital Mammograms. IEEE Journal of Translational Engineering in Health and Medicine 2022;10:1 View
  51. Hanis T, Arifin W, Haron J, Wan Abdul Rahman W, Ruhaiyem N, Abdullah R, Musa K. Factors Influencing Mammographic Density in Asian Women: A Retrospective Cohort Study in the Northeast Region of Peninsular Malaysia. Diagnostics 2022;12(4):860 View
  52. Tufail A, Ma Y, Kaabar M, Martínez F, Junejo A, Ullah I, Khan R, Liao I. Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions. Computational and Mathematical Methods in Medicine 2021;2021:1 View
  53. Tummala S, Kim J, Kadry S. BreaST-Net: Multi-Class Classification of Breast Cancer from Histopathological Images Using Ensemble of Swin Transformers. Mathematics 2022;10(21):4109 View
  54. Mohapatra S, Muduly S, Mohanty S, Ravindra J, Mohanty S. Evaluation of deep learning models for detecting breast cancer using histopathological mammograms Images. Sustainable Operations and Computers 2022;3:296 View
  55. Loizidou K, Elia R, Pitris C. Computer-aided breast cancer detection and classification in mammography: A comprehensive review. Computers in Biology and Medicine 2023;153:106554 View
  56. Asadi B, Memon Q. Efficient breast cancer detection via cascade deep learning network. International Journal of Intelligent Networks 2023;4:46 View
  57. Awotunde J, Panigrahi R, Khandelwal B, Garg A, Bhoi A. Breast cancer diagnosis based on hybrid rule-based feature selection with deep learning algorithm. Research on Biomedical Engineering 2023;39(1):115 View
  58. Jahangeer G, Thambidurai D. Detecting breast cancer using novel mask R‐CNN techniques. Expert Systems 2022;39(9) View
  59. Maqsood S, Damaševičius R, Maskeliūnas R. TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages. Applied Sciences 2022;12(7):3273 View
  60. Younis Y, Ali A, Alhafidhb O, Yahia W, Alazzam M, Hamad A, Meraf Z, Velmurugan P. Early Diagnosis of Breast Cancer Using Image Processing Techniques. Journal of Nanomaterials 2022;2022:1 View
  61. Avcı H, Karakaya J. A Novel Medical Image Enhancement Algorithm for Breast Cancer Detection on Mammography Images Using Machine Learning. Diagnostics 2023;13(3):348 View
  62. Wong C, Loke S, Lim H, Balasundaram G, Chan P, Chong B, Tan E, Lee A, Olivo M. Circulating microRNA breast cancer biomarker detection in patient sera with surface plasmon resonance imaging biosensor. Journal of Biophotonics 2021;14(11) View
  63. Zerouaoui H, Idri A. Reviewing Machine Learning and Image Processing Based Decision-Making Systems for Breast Cancer Imaging. Journal of Medical Systems 2021;45(1) View
  64. Gu D, Zhao W, Xie Y, Wang X, Su K, Zolotarev O. A Personalized Medical Decision Support System Based on Explainable Machine Learning Algorithms and ECC Features: Data from the Real World. Diagnostics 2021;11(9):1677 View
  65. Malebary S, Hashmi A. Automated Breast Mass Classification System Using Deep Learning and Ensemble Learning in Digital Mammogram. IEEE Access 2021;9:55312 View
  66. J. K. Jagadeesh Kumar S, Parthasarathi P, A. Hogo M, Masud M, F. Al-Amri J, Abouhawwash M. Breast Cancer Detection Using Breastnet-18 Augmentation with Fine Tuned Vgg-16. Intelligent Automation & Soft Computing 2023;36(2):2363 View
  67. Aref M, El-Gohary M, Elrewainy A, Mahmoud A, Aboughaleb I, Hussein A, El-Ghaffar S, Mahran A, El-Sharkawy Y. Emerging technology for intraoperative margin assessment and post-operative tissue diagnosis for breast-conserving surgery. Photodiagnosis and Photodynamic Therapy 2023;42:103507 View
  68. Khalil S, Nawaz U, Zubariah , Mushtaq Z, Arif S, ur Rehman M, Qureshi M, Malik A, Aleid A, Alhussaini K. Enhancing Ductal Carcinoma Classification Using Transfer Learning with 3D U-Net Models in Breast Cancer Imaging. Applied Sciences 2023;13(7):4255 View
  69. Ansar W, Raza B. Breast Cancer Segmentation in Mammogram Using Artificial Intelligence and Image Processing: A Systematic Review. Current Chinese Science 2023;3(1):3 View
  70. Albadr M, Ayob M, Tiun S, AL-Dhief F, Arram A, Khalaf S. Breast cancer diagnosis using the fast learning network algorithm. Frontiers in Oncology 2023;13 View
  71. Bhuyan H, Vijayaraj A, Ravi V. Diagnosis system for cancer disease using a single setting approach. Multimedia Tools and Applications 2023;82(30):46241 View
  72. Rani J, Singh J, Virmani J. Hybrid computer aided diagnostic system designs for screen film mammograms using DL‐based feature extraction and ML‐based classifiers. Expert Systems 2023;40(7) View
  73. Alloqmani A, Abushark Y, Khan A. Anomaly Detection of Breast Cancer Using Deep Learning. Arabian Journal for Science and Engineering 2023;48(8):10977 View
  74. Badawy E, ElNaggar R, Soliman S, Elmesidy D. Performance of AI-aided mammography in breast cancer diagnosis: Does breast density matter?. Egyptian Journal of Radiology and Nuclear Medicine 2023;54(1) View
  75. Mahmoud A, El-Sharkawy Y. Delineation and detection of breast cancer using novel label-free fluorescence. BMC Medical Imaging 2023;23(1) View
  76. Singh H, Sharma V, Singh D. Breast mass segmentation using mammographic data: a systematic review. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2023;11(6):2161 View
  77. Su X, Wang S. Is Magnetic Resonance Imaging (MRI) Still a Gold Standard to Detect Breast Cancer: A Meta-analysis. Current Medical Imaging Formerly Current Medical Imaging Reviews 2023;19(14) View
  78. Lokaj B, Pugliese M, Kinkel K, Lovis C, Schmid J. Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review. European Radiology 2023;34(3):2096 View
  79. Junyue C, Zeebaree D, Qingfeng C, Zebari D. Breast cancer diagnosis using hybrid AlexNet-ELM and chimp optimization algorithm evolved by Nelder-mead simplex approach. Biomedical Signal Processing and Control 2023;85:105053 View
  80. Sumner C, Kietzman A, Kadom N, Frigini A, Makary M, Martin A, McKnight C, Retrouvey M, Spieler B, Griffith B. Medical Malpractice and Diagnostic Radiology: Challenges and Opportunities. Academic Radiology 2024;31(1):233 View
  81. Kuttan G, Elayidom M. Review on Computer Aided Breast Cancer Detection and Diagnosis using Machine Learning Methods on Mammogram Image. Current Medical Imaging Formerly Current Medical Imaging Reviews 2023;19(12) View
  82. Amin A, Dinesh Acharya U, Prakashini K, Siddalingaswamy P. Decision Support System in Identification of Lesions in the Dense Breast on Digital Mammograms. Journal of Physics: Conference Series 2023;2571(1):012009 View
  83. El Haji H, Souadka A, Patel B, Sbihi N, Ramasamy G, Patel B, Ghogho M, Banerjee I. Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning–Based Models. JCO Clinical Cancer Informatics 2023;(7) View
  84. S. S. Prediction of Breast Cancer Through Random Forest. Current Medical Imaging Reviews 2023;19(10) View
  85. Qian L, Bai J, Huang Y, Zeebaree D, Saffari A, Zebari D. Breast cancer diagnosis using evolving deep convolutional neural network based on hybrid extreme learning machine technique and improved chimp optimization algorithm. Biomedical Signal Processing and Control 2024;87:105492 View
  86. Boudouh S, Bouakkaz M. New enhanced breast tumor detection approach in mammogram scans based on pre-processing and deep transfer learning techniques. Multimedia Tools and Applications 2023;83(9):27357 View
  87. Ghorbian M, Ghorbian S. Usefulness of machine learning and deep learning approaches in screening and early detection of breast cancer. Heliyon 2023;9(12):e22427 View
  88. von Coburg E, Dunst S. The adverse outcome pathway for breast cancer: a knowledge management framework bridging biomedicine and toxicology. Discover Oncology 2023;14(1) View
  89. Krishnan N, Muthu P. CVINet: A deep learning based model for the diagnosis of chronic venous insufficiency in lower extremity using infrared thermal images. International Journal of Imaging Systems and Technology 2024;34(2) View
  90. Saad Y, Meyer J. Quantifying Levels of Influence and Causal Responsibility in Dynamic Decision Making Events. ACM Transactions on Intelligent Systems and Technology 2024;15(1):1 View
  91. Meenalochini G, Ramkumar S. A Deep Learning Based Breast Cancer Classification System Using Mammograms. Journal of Electrical Engineering & Technology 2024;19(4):2637 View
  92. Aviles-Yataco W, Meneses-Claudio B. Redes neuronales aplicadas a la detección y diagnóstico del Cáncer de Mama, una revisión sistemática de la literatura científica de los últimos 5 años. Salud, Ciencia y Tecnología - Serie de Conferencias 2022;1:35 View
  93. Liu W, Wei R, Chen J, Li Y, Pang H, Zhang W, An C, Li C. Prognosis prediction and risk stratification of transarterial chemoembolization or intraarterial chemotherapy for unresectable hepatocellular carcinoma based on machine learning. European Radiology 2024 View
  94. Umamaheswari T, Murali Mohanbabu Y. CNN-FS-IFuzzy: A new enhanced learning model enabled by adaptive tumor segmentation for breast cancer diagnosis using 3D mammogram images. Knowledge-Based Systems 2024;288:111443 View
  95. Meenalochini G, Guka D, Sivasakthivel R, Rajagopal M. A Progressive UNDML Framework Model for Breast Cancer Diagnosis and Classification. Data and Metadata 2024;3:198 View
  96. Albadr M, AL-Dhief F, Man L, Arram A, Abbas A, Homod R. Online sequential extreme learning machine approach for breast cancer diagnosis. Neural Computing and Applications 2024;36(18):10413 View
  97. Manimurugan S, Karthikeyan P, Aborokbah M, Narmatha C, Ganesan S. Breast cancer diagnosis model using stacked autoencoder with particle swarm optimization. Ain Shams Engineering Journal 2024;15(6):102734 View
  98. AlSalman H, Al-Rakhami M, Alfakih T, Hassan M. Federated Learning Approach for Breast Cancer Detection Based on DCNN. IEEE Access 2024;12:40114 View
  99. Anbumani A, Jayanthi P. Classification of mammogram breast cancer using customized deep learning model. Journal of Intelligent & Fuzzy Systems 2024:1 View
  100. P.J S, S S, M M, T K. Hybrid deep learning enabled breast cancer detection using mammogram images. Biomedical Signal Processing and Control 2024;95:106310 View
  101. Baroni G, Rasotto L, Roitero K, Tulisso A, Di Loreto C, Della Mea V. Optimizing Vision Transformers for Histopathology: Pretraining and Normalization in Breast Cancer Classification. Journal of Imaging 2024;10(5):108 View
  102. Iglesias G, Talavera E, Troya J, Díaz-Álvarez A, García-Remesal M. Artificial intelligence model for tumoral clinical decision support systems. Computer Methods and Programs in Biomedicine 2024;253:108228 View
  103. Sindhura D, Pai R, Bhat S, Pai M. A review of deep learning and Generative Adversarial Networks applications in medical image analysis. Multimedia Systems 2024;30(3) View
  104. kadhim ajlan I, Murad H, Salim A, fadhil bin yousif A. Extreme Learning machine algorithm for breast Cancer diagnosis. Multimedia Tools and Applications 2024 View

Books/Policy Documents

  1. Mahapatra D, Ray R, Dash S. Technical Advancements of Machine Learning in Healthcare. View
  2. Sengupta D. Machine Learning, Big Data, and IoT for Medical Informatics. View
  3. Singh V, Rashwan H, Abdel-Nasser M, Akram F, Haffar R, Pandey N, Arenas M, Romani S, Puig D. State of the Art in Neural Networks and their Applications. View
  4. Lakshminarayanan K, Robinson Y, Vimal S, Kang D. Advances in Artificial Intelligence and Applied Cognitive Computing. View
  5. Mohapatra S, Muduly S, Mohanty S, Moharana S. Meta Heuristic Techniques in Software Engineering and Its Applications. View
  6. Kriti , Virmani J, Agarwal R. Biomedical Signal and Image Processing with Artificial Intelligence. View
  7. Carrera E, Sandoval B, Carrasco C. Advanced Research in Technologies, Information, Innovation and Sustainability. View
  8. Saghazadeh A, Rezaei N. Handbook of Cancer and Immunology. View
  9. Sneha S, Bharathi M. Second International Conference on Image Processing and Capsule Networks. View
  10. Kameswari S, Vijayakumar V. Evolution in Signal Processing and Telecommunication Networks. View
  11. Sahu A, Qazi S, Raza K, Singh A, Verma S. Computational Intelligence in Oncology. View
  12. Barua R, Mondal J. Machine Learning and AI Techniques in Interactive Medical Image Analysis. View
  13. Yuan H, Yan Y, Dong S. Artificial Neural Networks and Machine Learning – ICANN 2023. View
  14. Kriti , Narula S, Kaur S, Agarwal R. Recent Advances in Metrology. View
  15. Kumar B, Tamkute P, Saurabh K, Mishra A, Kumar S, Talesara A, Vyas O. Robotics, Control and Computer Vision. View
  16. Das J, Pramanik S, Bhattacharjee D. Proceedings of International Conference on Data, Electronics and Computing. View
  17. Baroni G, Rasotto L, Roitero K, Siraj A, Della Mea V. Image Analysis and Processing - ICIAP 2023 Workshops. View
  18. Panambur A, Yu H, Bhat S, Madhu P, Bayer S, Maier A. Bildverarbeitung für die Medizin 2024. View
  19. Mohammed Z, Hussam F, Al-Dulaimi M, Arya P. Data Science and Big Data Analytics. View
  20. Stockton T, Peddle B, Gaulin A, Wiechert E, Lu W. Advanced Information Networking and Applications. View