Skinguard-Ai FOR Preliminary Diagnosis OF Dermatological Manifestations

Authors

  • Dr. M. Thejovathi Associate Professor, Department of CSE (Artificial Intelligence & Machine Learning)
  • K. Jayasri Assistant Professor, Department of CSE, Aurora Deemed To Be University, Hyderabad, Telangana - 500098, India
  • K. Munni B.Tech 4th Year Students, Department of CSE (Artificial Intelligence & Machine Learning), Vignan’s Institute of Management and Technology for Women, Hyderabad, Telangana - 501301, India
  • B. Pooja B.Tech 4th Year Students, Department of CSE (Artificial Intelligence & Machine Learning), Vignan’s Institute of Management and Technology for Women, Hyderabad, Telangana - 501301, India
  • B. Madhuri B.Tech 4th Year Students, Department of CSE (Artificial Intelligence & Machine Learning), Vignan’s Institute of Management and Technology for Women, Hyderabad, Telangana - 501301, India
  • S. Meghana Priya B.Tech 4th Year Students, Department of CSE (Artificial Intelligence & Machine Learning), Vignan’s Institute of Management and Technology for Women, Hyderabad, Telangana - 501301, India

DOI:

https://doi.org/10.63278/mme.vi.1664

Keywords:

Skin Disease Detection, CNN, Image Classification, LIME, Explainable AI, Skincare Recommendations, Deep Learning.

Abstract

We introduce an AI-based diagnostic system that supports the initial analysis of dermatological conditions based on clinical skin photos. The algorithm makes a disease prediction and confidence score after processing the input image. A score closer to 1 shows a high level of certainty that it is that type of diagnosis, while lower values tend to indicate more ambiguous classifications. The technology also marks features in vital areas of the image that had the most impact on the model’s final class of the image, providing visual interpretability using explainable AI methods.

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How to Cite

Thejovathi, Dr. M., K. Jayasri, K. Munni, B. Pooja, B. Madhuri, and S. Meghana Priya. 2025. “Skinguard-Ai FOR Preliminary Diagnosis OF Dermatological Manifestations”. Metallurgical and Materials Engineering, May, 912-16. https://doi.org/10.63278/mme.vi.1664.

Issue

Vol. 31 No. 5 (2025)

Section

Research

License

Copyright (c) 2025 Dr. M. Thejovathi, K. Jayasri, K. Munni, B. Pooja, B. Madhuri, S. Meghana Priya

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