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

New Methods (547) Review (474) Clinical Information and Decision Making (575) Machine Learning (663)

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 Brinker 1, 2 Author Orcid Image ;   Achim Hekler 2 Author Orcid Image ;   Jochen Sven Utikal 3, 4 Author Orcid Image ;   Niels Grabe 5 Author Orcid Image ;   Dirk Schadendorf 6 Author Orcid Image ;   Joachim Klode 6 Author Orcid Image ;   Carola Berking 7 Author Orcid Image ;   Theresa Steeb 7 Author Orcid Image ;   Alexander H Enk 1 Author Orcid Image ;   Christof von Kalle 2 Author Orcid Image
Article Authors Cited by (104) 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, Jansen P, Klode J, Lodde G, Sammet S, Schadendorf D, 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, Berking C, Eicher L, Hartmann D, 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, Haferkamp S, 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, Hauschild A, 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, Berking C, 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, Haferkamp S, Halupczok A, Hegemann M, Dinauer V, Maagk M, Mickler M, Philipp B, Wilm A, Wittmann C, Gesierich A, Glutsch V, Kahlert K, Kerstan A, Schilling B, 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, Klode J, Lodde G, Sammet S, Schadendorf D, 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, Berking C, 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, Haferkamp S, 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. 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, 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, Hauschild A, 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, Berking C, 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, Haferkamp S, Halupczok A, Hegemann M, Dinauer V, Maagk M, Mickler M, Philipp B, Wilm A, Wittmann C, Gesierich A, Glutsch V, Kahlert K, Kerstan A, Schilling B, 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 2021:1 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, 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;1022:012035 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 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 2021:20210002 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 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 2021 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