A study on Detection of Skin Diseases from Digital Images

Author(s)	Shashidhara B, Gururaj J P, Dadavali S P
Country	India
Abstract	ABSTRACT Skin diseases are very common, but early diagnosis is difficult because specialists are few, treatment is costly, and many diseases look alike. This work presents a survey of deep learning methods used for automatic skin disease detection from digital images. Twenty-three studies published between 2017 and 2025 were reviewed and compared on the basis of dataset, model architecture and reported results. The survey shows that Convolutional Neural Networks dominate this field and perform far better than general physicians, and almost equal to expert dermatologists, on well-defined tasks. However, it is observed that accuracy falls sharply on external data, ordinary clinical photographs and darker skin tones, and that high overall accuracy often hides poor detection of dangerous classes such as melanoma. Based on this analysis, our opinion is that such systems should be used only as fast, low-cost early screening and decision-support tools, and that future work must focus on balanced datasets, per-class reporting, explainability and external validation before clinical use.
Keywords	Keywords: skin disease detection, deep learning, CNN, survey, transfer learning, explainable AI
Field	Computer Applications
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-03-10
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.86555

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