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Erythemato-squamous skin diseases are difficult to diagnose due to overlapping symptoms and manual diagnosis delays. Automated prediction using image analysis and machine learning accelerates treatment planning, potentially saving numerous lives. In recent years, combining image processing techniques and machine learning algorithms has shown a promising approach to diagnose certain dermatological conditions quickly, accurately, and effectively. This review looks at developments in feature selection, segmentation, and classification, emphasizing their strengths, limitations, and future research directions in the field of dermatology and medical image analysis.

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Advancements in Dermatological Diagnosis: A Review on Erythemato-squamous Skin Diseases Utilizing Image Processing and Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Tanisa Mallick,
  • Saikat Basu,
  • Koushik Majumder

摘要

Erythemato-squamous skin diseases are difficult to diagnose due to overlapping symptoms and manual diagnosis delays. Automated prediction using image analysis and machine learning accelerates treatment planning, potentially saving numerous lives. In recent years, combining image processing techniques and machine learning algorithms has shown a promising approach to diagnose certain dermatological conditions quickly, accurately, and effectively. This review looks at developments in feature selection, segmentation, and classification, emphasizing their strengths, limitations, and future research directions in the field of dermatology and medical image analysis.