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

Digital Health Reviews (1318) Machine Learning (1491) Imaging Informatics (198) Skin Cancer and Melanoma Prevention (112) Innovations and Technology in Cancer Care (429) Artificial Intelligence (1409)

Published on in Vol 23, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20708, first published .
Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Authors of this article:

Julia Höhn1 Author Orcid Image ;   Achim Hekler1 Author Orcid Image ;   Eva Krieghoff-Henning1 Author Orcid Image ;   Jakob Nikolas Kather2, 3 Author Orcid Image ;   Jochen Sven Utikal4, 5 Author Orcid Image ;   Friedegund Meier6 Author Orcid Image ;   Frank Friedrich Gellrich6 Author Orcid Image ;   Axel Hauschild7 Author Orcid Image ;   Lars French8 Author Orcid Image ;   Justin Gabriel Schlager8 Author Orcid Image ;   Kamran Ghoreschi9 Author Orcid Image ;   Tabea Wilhelm9 Author Orcid Image ;   Heinz Kutzner10 Author Orcid Image ;   Markus Heppt11 Author Orcid Image ;   Sebastian Haferkamp12 Author Orcid Image ;   Wiebke Sondermann13 Author Orcid Image ;   Dirk Schadendorf13 Author Orcid Image ;   Bastian Schilling14 Author Orcid Image ;   Roman C Maron1 Author Orcid Image ;   Max Schmitt1 Author Orcid Image ;   Tanja Jutzi1 Author Orcid Image ;   Stefan Fröhling3, 15 Author Orcid Image ;   Daniel B Lipka3, 15, 16 Author Orcid Image ;   Titus Josef Brinker1 Author Orcid Image
Article Authors Cited by (39) Tweetations (5) Metrics

Journals

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  2. Rajeshwari J, Sughasiny M. Skin Cancer Severity Prediction Model Based on Modified Deep Neural Network with Horse Herd Optimization. Optical Memory and Neural Networks 2022;31(2):206 View
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  4. Zhang J, Ahmed S. [Retracted] Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network. Computational Intelligence and Neuroscience 2021;2021(1) View
  5. Cai G, Zhu Y, Wu Y, Jiang X, Ye J, Yang D. A multimodal transformer to fuse images and metadata for skin disease classification. The Visual Computer 2023;39(7):2781 View
  6. Tasci E, Zhuge Y, Camphausen K, Krauze A. Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets. Cancers 2022;14(12):2897 View
  7. Sitaru S, Zink A. Digitalisierung in der Dermatoonkologie: künstliche Intelligenz zur Diagnostik. best practice onkologie 2023;18(1-2):20 View
  8. Naeem A, Anees T, Fiza M, Naqvi R, Lee S. SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images. Sensors 2022;22(15):5652 View
  9. Qu D, Deng D, Ahmed S. [Retracted] Construction of Community Life Service in the Sharing Economy Based on Deep Neural Network. Computational Intelligence and Neuroscience 2021;2021(1) View
  10. Kaur R, GholamHosseini H, Sinha R, Lindén M. Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images. Sensors 2022;22(3):1134 View
  11. Sangers T, Wakkee M, Moolenburgh F, Nijsten T, Lugtenberg M. Towards successful implementation of artificial intelligence in skin cancer care: a qualitative study exploring the views of dermatologists and general practitioners. Archives of Dermatological Research 2022 View
  12. Raza R, Zulfiqar F, Tariq S, Anwar G, Sargano A, Habib Z. Melanoma Classification from Dermoscopy Images Using Ensemble of Convolutional Neural Networks. Mathematics 2021;10(1):26 View
  13. 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
  14. Saarela M, Geogieva L. Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model. Applied Sciences 2022;12(19):9545 View
  15. Carvalho R, Morgado A, Andrade C, Nedelcu T, Carreiro A, Vasconcelos M. Integrating Domain Knowledge into Deep Learning for Skin Lesion Risk Prioritization to Assist Teledermatology Referral. Diagnostics 2021;12(1):36 View
  16. Tang Q, Ahmed S. [Retracted] Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network. Computational Intelligence and Neuroscience 2021;2021(1) View
  17. Oloruntoba A, Vestergaard T, Nguyen T, Yu Z, Sashindranath M, Betz-Stablein B, Soyer H, Ge Z, Mar V. Assessing the Generalizability of Deep Learning Models Trained on Standardized and Nonstandardized Images and Their Performance Against Teledermatologists: Retrospective Comparative Study. JMIR Dermatology 2022;5(3):e35150 View
  18. Rajeshwari J, Sughasiny M. Modified PNN classifier for diagnosing skin cancer severity condition using SMO optimization technique. AIMS Electronics and Electrical Engineering 2022;7(1):75 View
  19. 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
  20. Fogelberg K, Chamarthi S, Maron R, Niebling J, Brinker T. Domain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation. New Biotechnology 2023;76:106 View
  21. Corbin A, Marques O. Assessing Bias in Skin Lesion Classifiers With Contemporary Deep Learning and Post-Hoc Explainability Techniques. IEEE Access 2023;11:78339 View
  22. Lai W, Kuang M, Wang X, Ghafariasl P, Sabzalian M, Lee S. Skin cancer diagnosis (SCD) using Artificial Neural Network (ANN) and Improved Gray Wolf Optimization (IGWO). Scientific Reports 2023;13(1) View
  23. Llamas-Velasco M, Ovejero-Merino E. Inteligencia artificial en el diagnóstico dermatopatológico. Piel 2024;39(8):512 View
  24. Lyakhov P, Lyakhova U, Kalita D. Multimodal Analysis of Unbalanced Dermatological Data for Skin Cancer Recognition. IEEE Access 2023;11:131487 View
  25. Myslicka M, Kawala-Sterniuk A, Bryniarska A, Sudol A, Podpora M, Gasz R, Martinek R, Kahankova Vilimkova R, Vilimek D, Pelc M, Mikolajewski D. Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes. Archives of Dermatological Research 2024;316(4) View
  26. Malik F, Yousaf M, Sial H, Viriri S. Exploring dermoscopic structures for melanoma lesions' classification. Frontiers in Big Data 2024;7 View
  27. Pacal I, Alaftekin M, Zengul F. Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP. Journal of Imaging Informatics in Medicine 2024;37(6):3174 View
  28. Lyakhova U, Lyakhov P. Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects. Computers in Biology and Medicine 2024;178:108742 View
  29. Abbas S, Asif M, Rehman A, Alharbi M, Khan M, Elmitwally N. Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review. Heliyon 2024;10(17):e36743 View
  30. Afraz A, Chashmyazdan M, Khajouei R, Bagherinezhad Z. Literature Searches in Medical Informatics Systematic Reviews: Suggested Approaches. Medical Reference Services Quarterly 2024:1 View

Books/Policy Documents

  1. 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
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  3. Bhullar P, Murphree D, Choudhary A, Peters M, Sokumbi O, Comfere N. Artificial Intelligence in Clinical Practice. View
  4. Regitz-Zagrosek V. Gendermedizin in der klinischen Praxis. View
  5. Bindhu A, Thanammal K. Evolution in Computational Intelligence. View