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The diagnosis of skin diseases is one of the most challenging yet important tasks that should be done with precision and speed as it concerns public health. In this work, we propose an improved deep learning model that integrates MobileNet with convolutional neural network (CNN) for effective and fast skin diseases detection. Using HAM10000 dataset, we obtained an accuracy of 95%. The model is tailored for instantaneous predictions making it ideal for implementation in mobile and low resource settings. We carried out empirical studies to evaluate a method’s effectiveness and compare it with the alternatives available.

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Skin Disease Identification

  • 会议论文

  • 发表日期：

    2026年

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  • Mothe Suneetha,
  • Gudipudi Sony,
  • Tummalapenta Vineesha,
  • Gade Salini,
  • S. N. Tirumalarao,
  • Moturi Sireesha

摘要

The diagnosis of skin diseases is one of the most challenging yet important tasks that should be done with precision and speed as it concerns public health. In this work, we propose an improved deep learning model that integrates MobileNet with convolutional neural network (CNN) for effective and fast skin diseases detection. Using HAM10000 dataset, we obtained an accuracy of 95%. The model is tailored for instantaneous predictions making it ideal for implementation in mobile and low resource settings. We carried out empirical studies to evaluate a method’s effectiveness and compare it with the alternatives available.