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Skin infections in today’s era are proven to be very deadly if not treated at an early stage. They can gradually results in skin cancer. Doctors use a computer-assisted diagnostic support system to help detect these at an early stage so that the patient’s life can be saved. The research in these areas is essential for the development of effective treatments, prevention strategies, and improved patient outcomes in the field of skin infections. In this work, a deep learning model is developed to identify skin infections at a precision of near about 92%. The model is evaluated using a dataset that includes a very large number of samples of mainly 7 types of skin cancers that are classified as dangerous. By using CNN with transfer learning techniques, the class imbalance and complexity in the dataset are reduced, and the model performs better than most of the deep learning techniques in terms of accuracy and computational cost.

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A Novel Approach for Skin Infections Classification Using Transfer Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Dhruv Singhal,
  • Deepanshu Verma,
  • Ankita Nainwal,
  • B. Dhananjaya,
  • Garima Sharma

摘要

Skin infections in today’s era are proven to be very deadly if not treated at an early stage. They can gradually results in skin cancer. Doctors use a computer-assisted diagnostic support system to help detect these at an early stage so that the patient’s life can be saved. The research in these areas is essential for the development of effective treatments, prevention strategies, and improved patient outcomes in the field of skin infections. In this work, a deep learning model is developed to identify skin infections at a precision of near about 92%. The model is evaluated using a dataset that includes a very large number of samples of mainly 7 types of skin cancers that are classified as dangerous. By using CNN with transfer learning techniques, the class imbalance and complexity in the dataset are reduced, and the model performs better than most of the deep learning techniques in terms of accuracy and computational cost.