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

Digital Health Reviews (1289) Machine Learning (1465) Imaging Informatics (193) Skin Cancer and Melanoma Prevention (112) Innovations and Technology in Cancer Care (423) Artificial Intelligence (1371)

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 (38) Tweetations (5) Metrics

Journals

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  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
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  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
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  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
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  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
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  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

Books/Policy Documents

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  2. Fernandez K, Young A, Bhattarcharya A, Kusari A, Wei M. Teledermatology. View
  3. Bhullar P, Murphree D, Choudhary A, Peters M, Sokumbi O, Comfere N. Artificial Intelligence in Clinical Practice. View
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