ViT-enhanced ai-powered deep learning framework for skin disease ‎diagnosis

Authors

  • Dhiraj Chavan Dr. D. Y. Patil Institute of Engineering, Management and ‎Research, Akurdi, Pune - 44, India
  • Prathamesh Sonawane Dr. D. Y. Patil Institute of Engineering, Management and ‎Research, Akurdi, Pune - 44, India
  • Neha Jadhav Dr. D. Y. Patil Institute of Engineering, Management and ‎Research, Akurdi, Pune - 44, India
  • Janhavi Naidu Dr. D. Y. Patil Institute of Engineering, Management and ‎Research, Akurdi, Pune - 44, India
  • Mital Kadu Dr. D. Y. Patil International University, Akurdi, Pune - 44, India‎
  • Vibhavari Jawale Dr. D. Y. Patil International University, Akurdi, Pune - 44, India‎

How to Cite

Chavan, D., Sonawane, P. ., Jadhav, N., Naidu, J., Kadu, M., & Jawale, V. (2025). ViT-enhanced ai-powered deep learning framework for skin disease ‎diagnosis. International Journal of Advanced Mathematical Sciences, 11(1), 73-76. https://doi.org/10.14419/w0p98q32

DOI:

https://doi.org/10.14419/w0p98q32

Abstract

Skin concerns are a rising health issue globally, and accurate detection and diagnosis of these issues are key ‎in preventing serious consequences. We provide a complete overview of deep learning approaches to ‎dermatoscopic image classification, specifically focusing on the newly developed Vision Transformer (ViT) ‎approaches. We discuss the advantages of using ViT approaches in skin disease classification versus prior deep ‎learning approaches, specifically, convolutional neural networks (CNNs). We analyse four commonly reported ‎skin conditions: Basal Cell Carcinoma, Benign Keratosis, Eczema, and Melanoma. In doing so, we explore the ‎current literature and datasets available and summarize the advancements of artificial intelligence (AI) in ‎dermatology, identify potentially the most effective designs, and consider their incorporation into clinical ‎populations. This review is intended to provide insight into the current developments in the design of AI-‎AI-assisted automated skin disease diagnosis processes, including important trends, performance, and efficiencies ‎of models, and the current trends in skin disease diagnosis. In an ideal world, this review will provide a ‎foundation for the development of more accurate and ultimately less expensive diagnostics to enhance patient ‎care in dermatology.

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How to Cite

Chavan, D., Sonawane, P. ., Jadhav, N., Naidu, J., Kadu, M., & Jawale, V. (2025). ViT-enhanced ai-powered deep learning framework for skin disease ‎diagnosis. International Journal of Advanced Mathematical Sciences, 11(1), 73-76. https://doi.org/10.14419/w0p98q32