A Systematic Review of Machine Learning, Deep Learning, and Transfer Learning Methods for Skin Disease Classification

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Systematic review highlights machine learning, deep learning, and transfer learning for skin disease diagnostics, suggesting effective AI tools.

Deep learning models improve skin disease classification accuracy significantly, achieving rates of up to 100%.
Machine learning techniques like CNNs and transfer learning utilize large datasets to enhance the efficiency of dermatological diagnosis.
This systematic review assesses traditional and modern methods for skin disease detection and their implications for clinical practice.
Ongoing challenges in algorithmic bias and data standardization may hinder the effectiveness of AI in diverse patient populations.

Sambasivarao et al. (2025) studied this question.

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Systematic review highlights machine learning, deep learning, and transfer learning for skin disease diagnostics, suggesting effective AI tools.