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Bioengineering
Volume 13
Issue 8
10.3390/bioengineering13080872
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Review

Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review

by
Bhagyashri S. Sonune
1,
Udayakumar Ramanathan
1,
Dhiraj P. Tulaskar
2,
Shon G. Nemane
2,
Madhusudan B. Kulkarni
3,*,
Prakash Rewatkar
4 and
Manish Bhaiyya
2,*
1
Department of Computer Science and Information Technology, Kalinga University, Raipur 492101, CG, India
2
Department of Electronics and Telecommunication Engineering, Shri Sant Gajanan Maharaj College of Engineering, Shegaon 444203, MH, India
3
Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
4
Department of Mechanical Engineering, Israel Institute of Technology, Haifa 3200003, Israel
*
Authors to whom correspondence should be addressed.
Bioengineering 2026, 13(8), 872; https://doi.org/10.3390/bioengineering13080872
Submission received: 21 May 2026 / Revised: 2 July 2026 / Accepted: 3 July 2026 / Published: 28 July 2026
(This article belongs to the Special Issue Deep Learning for Medical Applications: Challenges and Opportunities)
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Abstract

Detection of skin cancer has become an increasingly prevalent health issue for which there is a need for reliable methods of detection to improve patient outcomes, minimize delays in diagnosis, and ensure clinical referral. Advances in computer vision and artificial intelligence have allowed automatic analyses of dermoscopic, clinical, and smartphone imaging of skin lesions. While numerous models have been found to perform very well on the basis of curated benchmark datasets, their accuracy in practical settings still needs to be evaluated. This review critically evaluates the literature from 2015 to 2025. Rather than considering the Dice, Jaccard, AUC, sensitivity, and specificity metrics on an absolute basis, this review evaluates them relative to dataset quality, validation process, external testing, statistical analysis, and risk of bias. Key areas of focus include dataset imbalance, lack of coverage of darker skin, poor external validation, explainability, uncertainty quantification, and barriers to clinical adoption. This review provides valuable insights for researchers, practitioners, dataset producers, and healthcare providers involved in developing AI-enabled dermatology tools.
Keywords: skin lesion; cancer; computer vision; artificial intelligence (AI); detection; dermatology skin lesion; cancer; computer vision; artificial intelligence (AI); detection; dermatology

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MDPI and ACS Style

Sonune, B.S.; Ramanathan, U.; Tulaskar, D.P.; Nemane, S.G.; Kulkarni, M.B.; Rewatkar, P.; Bhaiyya, M. Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review. Bioengineering 2026, 13, 872. https://doi.org/10.3390/bioengineering13080872

AMA Style

Sonune BS, Ramanathan U, Tulaskar DP, Nemane SG, Kulkarni MB, Rewatkar P, Bhaiyya M. Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review. Bioengineering. 2026; 13(8):872. https://doi.org/10.3390/bioengineering13080872

Chicago/Turabian Style

Sonune, Bhagyashri S., Udayakumar Ramanathan, Dhiraj P. Tulaskar, Shon G. Nemane, Madhusudan B. Kulkarni, Prakash Rewatkar, and Manish Bhaiyya. 2026. "Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review" Bioengineering 13, no. 8: 872. https://doi.org/10.3390/bioengineering13080872

APA Style

Sonune, B. S., Ramanathan, U., Tulaskar, D. P., Nemane, S. G., Kulkarni, M. B., Rewatkar, P., & Bhaiyya, M. (2026). Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review. Bioengineering, 13(8), 872. https://doi.org/10.3390/bioengineering13080872

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