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Skin diseases represent a considerable part of the world health problem, as millions of people are affected by skin diseases,
and skin diseases are visually similar and without easy access to dermatologists, present problems in terms of diagnostics. Convolutional
Neural Networks (CNNs) and Artificial Intelligence (AI) in general have been the game-changer of automated skin disease detection.
This paper aims to provide an overview of recent advances in the field of CNN and Transfer Learning (TL) approaches to classifying
and diagnosing dermatological diseases based on dermoscopic and clinical images. It discusses datasets, preprocessing pipelines, CNN
architectures, performance of different models, existing shortcomings, as well as future works. The key point of this review is to aid in
creating AI-based web apps that would provide safe and efficient screening of skin diseases.
Keywords:
Skin Disease Detection, Convolutional Neural Networks (CNNs), Transfer Learning, Medical Image Classification, Dermoscopic Image Analysis.
Cite Article:
"A Comprehensive Review on Skin Disease Detection Using Convolutional Neural Networks and Transfer Learning Approaches ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.b679-b684, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506182.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator