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

Image Recognition and AI in Dermatology (11) Registered Report (560) Artificial Intelligence (1371) Skin Cancer and Melanoma Prevention (112)

Published on in Vol 5, No 3 (2022): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39143, first published .
Improving Skin Color Diversity in Cancer Detection: Deep Learning Approach

Improving Skin Color Diversity in Cancer Detection: Deep Learning Approach

Improving Skin Color Diversity in Cancer Detection: Deep Learning Approach

Authors of this article:

Eman Rezk1 Author Orcid Image ;   Mohamed Eltorki2 Author Orcid Image ;   Wael El-Dakhakhni1 Author Orcid Image
Article Authors Cited by (20) Tweetations (10) Metrics

Journals

  1. Grignaffini F, Barbuto F, Piazzo L, Troiano M, Simeoni P, Mangini F, Pellacani G, Cantisani C, Frezza F. Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review. Algorithms 2022;15(11):438 View
  2. Kushimo O, Salau A, Adeleke O, Olaoye D. Deep learning model to improve melanoma detection in people of color. Arab Journal of Basic and Applied Sciences 2023;30(1):92 View
  3. Plaisime M. Invited Commentary: Undiagnosed and Undertreated—the Suffocating Consequences of the Use of Racially Biased Medical Devices During the COVID-19 Pandemic. American Journal of Epidemiology 2023;192(5):714 View
  4. Debelee T. Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review. Diagnostics 2023;13(19):3147 View
  5. Rezk E, Haggag M, Eltorki M, El-Dakhakhni W. A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges. Healthcare Analytics 2023;4:100259 View
  6. Grignaffini F, Troiano M, Barbuto F, Simeoni P, Mangini F, D’Andrea G, Piazzo L, Cantisani C, Musolff N, Ricciuti C, Frezza F. Anomaly Detection for Skin Lesion Images Using Convolutional Neural Network and Injection of Handcrafted Features: A Method That Bypasses the Preprocessing of Dermoscopic Images. Algorithms 2023;16(10):466 View
  7. Jebeile J, Lam V, Majszak M, Räz T. Machine learning and the quest for objectivity in climate model parameterization. Climatic Change 2023;176(8) View
  8. Nguyen A, Ha S. CNN-BiLSTM and Time Delay Embedding: A Single-Step Hybrid Deep Learning Model for Stock Price Forecasting. SSRN Electronic Journal 2024 View
  9. Wen D, Soltan A, Trucco E, Matin R. From data to diagnosis: skin cancer image datasets for artificial intelligence. Clinical and Experimental Dermatology 2024;49(7):675 View
  10. Khatun N, Spinelli G, Colecchia F. Technology innovation to reduce health inequality in skin diagnosis and to improve patient outcomes for people of color: a thematic literature review and future research agenda. Frontiers in Artificial Intelligence 2024;7 View
  11. Alipour N, Burke T, Courtney J. Skin Type Diversity in Skin Lesion Datasets: A Review. Current Dermatology Reports 2024;13(3):198 View
  12. Adamu S, Alhussian H, Aziz N, Abdulkadir S, Alwadin A, Abubakar Imam A, Abdullahi M, Garba A, Saidu Y. The future of skin cancer diagnosis: a comprehensive systematic literature review of machine learning and deep learning models. Cogent Engineering 2024;11(1) View
  13. Rasel M, Kareem S, Obaidellah U. Integrating color histogram analysis and convolutional neural networks for skin lesion classification. Computers in Biology and Medicine 2024;183:109250 View

Books/Policy Documents

  1. Barros L, Chaves L, Avila S. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. View
  2. Kim A, Saharkhiz N, Sizikova E, Lago M, Sahiner B, Delfino J, Badano A. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. View