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

Image Recognition and AI in Dermatology (20) Registered Report (791) Artificial Intelligence (2129) Skin Cancer and Melanoma Prevention (123)

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 (40) 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, Nguyen P. BiConNet: A Time-Delayed Hybrid Deep Learning Model for Equity Price Prediction. 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
  14. Hamrani A, Leizaola D, Reddy Vedere N, Kirsner R, Kaile K, Trinidad A, Godavarty A. AI Dermatochroma Analytica (AIDA): Smart Technology for Robust Skin Color Classification and Segmentation. Cosmetics 2024;11(6):218 View
  15. Rezk E, Eltorki M, El-Dakhakhni W. Human knowledge-based artificial intelligence methods for skin cancer management: Accuracy and interpretability study. Smart Health 2025;36:100540 View
  16. Saeidnia H, Firuzpour F, Kozak M, majd H. Advancing cancer diagnosis and treatment: integrating image analysis and AI algorithms for enhanced clinical practice. Artificial Intelligence Review 2025;58(4) View
  17. Shaw D, Lorenzini G, Arbelaez Ossa L, Eckstein J, Steiner L, Elger B. When and what patients need to know about AI in clinical care. Swiss Medical Weekly 2025;155(1):4013 View
  18. Mei L, Cao M, Li J, Ye X, Liu X, Yang G. Deep learning in assisting dermatologists in classifying basal cell carcinoma from seborrheic keratosis. Frontiers in Oncology 2025;15 View
  19. Gopal J, Zhou A, Marghoob A, Gronbeck C, Grant-Kels J. The state of artificial intelligence-enabled skin cancer diagnostics: Why are there two spectroscopy devices available yet no imaging devices?. Clinics in Dermatology 2025;43(5):687 View
  20. Starke C, Blanke T, Helberger N, Smets S, de Vreese C. Interdisciplinary Perspectives on the (Un)fairness of Artificial Intelligence. Minds and Machines 2025;35(2) View
  21. Deng J, Elghobashy M, Zang K, Patel S, Guo E, Heybati K. So You’ve Got a High AUC, Now What? An Overview of Important Considerations when Bringing Machine-Learning Models from Computer to Bedside. Medical Decision Making 2025;45(6):640 View
  22. Hammimou A, Ezzahori H, Boudaoud A, Aqil M. From traditional to deep learning methods for skin lesion segmentation: A literature review. Scientific African 2025;29:e02783 View
  23. Utti V, Bikia V, Agarwal A, Daneshjou R. Integrating Artificial Intelligence in Dermatological Cancer Screening and Diagnosis: Efficacy, Challenges, and Future Directions. Annual Review of Biomedical Data Science 2025;8(1):591 View
  24. Rosario N, Perkins K, Powell A, Wollen J. Student pharmacist accuracy and confidence in identifying dermatologic conditions in skin of color: a pilot study. Currents in Pharmacy Teaching and Learning 2026;18(1):102509 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
  3. Wang J, Chung Y, Ding Z, Hamm J. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 Workshops. View
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  5. Na A. Pattern Recognition. ICPR 2024 International Workshops and Challenges. View
  6. Ruga T, Zumpano E, Vocaturo E, Caroprese L, Arlia C. Computational Science – ICCS 2025 Workshops. View
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Conference Proceedings

  1. G D, K L, M M, K N, C T. 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS). Skin Cancer Detection Using Multi Class CNN Algorithm View
  2. Kaur G, Ekka A, Devanshu , Suman D. 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC). Racial Disparities in Performance of Artificial Intelligence Algorithms for Diagnosis of Common Dermatological Diseases View
  3. Korsak K, Hernes M, Walaszczyk E, Rot A, Grzęda A. 2023 13th International Conference on Advanced Computer Information Technologies (ACIT). Analysis of Skin Lesions for Cancer Detection Using Convolutional Neural Networks View
  4. Benmalek A, Cintas C, Tadesse G, Daneshjou R, Varshney K, Dalila C. 2024 IEEE International Symposium on Biomedical Imaging (ISBI). Evaluating the Impact of Skin Tone Representation on Out-of-Distribution Detection Performance in Dermatology View
  5. Mienye I, Obaido G, Emmanuel I, Ajani A. 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI). A Survey of Bias and Fairness in Healthcare AI View
  6. Na A. 2024 International Conference on Electrical, Communication and Computer Engineering (ICECCE). Improving Convolutional Neural Networks Diagnostic Ability for Malignant Skin Diseases on Diverse Skin Tones with Smooth Grad-CAM++ and Grad-CAM++ View
  7. Kalra K, Iyer V, Patel N. 2024 25th International Arab Conference on Information Technology (ACIT). Enhancing Skin Cancer Detection Across Skin Tones with Style Transfer Augmentation View
  8. Kaushik P, Sharma P. 2025 International Conference on Automation and Computation (AUTOCOM). Automated Skin Tone Classification Using InceptionV3: Enhancing Accuracy and Inclusivity in Image Recognition View
  9. Huang A. 2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC). Ethical Dimensions of Artificial Intelligence in Personalized Medicine: Navigating Challenges and Shaping Future Policies View