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This research provides an automated method that uses deep learning for the detection and classification of skin diseases, designed to improve the accessibility and efficiency of dermatological diagnostics. The system analyzes user-provided skin images to deliver accurate and reliable diagnoses, addressing the critical need for accessible health care in remote and underserved areas. By leveraging convolutional neural networks (CNNs) and MobileNet, the model processes images hierarchically, extracting features from local to global scales to identify subtle patterns that indicate malignancy or benignity. The use of CNNs enables the system to discern intricate patterns, while MobileNet’s lightweight architecture ensures computational efficiency, making the system well-suited for deployment on resource-constrained devices such as smartphones. This synergy guarantees scalability and accessibility while simultaneously decreasing diagnostic time, making it ideal for broad use. A versatile and reliable solution for dermatological care on a worldwide scale is on the horizon, to the model’s consistent high accuracy that it has learned from a diversified dataset. Improving the system in the future will include adding more diseases to the dataset and making real-time diagnostic recommendations.

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Deep Learning-Based Automated System for Accurate Skin Disease Diagnosis and Classification

  • 会议论文

  • 发表日期：

    2026年

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  • N. V. Ravindhar,
  • S. Ashmi,
  • S. Swetha,
  • J. S. Vithyashree

摘要

This research provides an automated method that uses deep learning for the detection and classification of skin diseases, designed to improve the accessibility and efficiency of dermatological diagnostics. The system analyzes user-provided skin images to deliver accurate and reliable diagnoses, addressing the critical need for accessible health care in remote and underserved areas. By leveraging convolutional neural networks (CNNs) and MobileNet, the model processes images hierarchically, extracting features from local to global scales to identify subtle patterns that indicate malignancy or benignity. The use of CNNs enables the system to discern intricate patterns, while MobileNet’s lightweight architecture ensures computational efficiency, making the system well-suited for deployment on resource-constrained devices such as smartphones. This synergy guarantees scalability and accessibility while simultaneously decreasing diagnostic time, making it ideal for broad use. A versatile and reliable solution for dermatological care on a worldwide scale is on the horizon, to the model’s consistent high accuracy that it has learned from a diversified dataset. Improving the system in the future will include adding more diseases to the dataset and making real-time diagnostic recommendations.