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Millions of individuals worldwide suffer from hair loss, which is a common and frequently distressing condition. Presently, dermatologists rely on visual assessments and subjective judgments to diagnose and classify hair loss. We present a novel approach to hair loss stage classification using facial pictures and machine learning algorithms in this research. We gathered a collection of facial photographs of people in various stages of hair loss and used computer vision algorithms to extract significant information from the images. We trained and evaluated a classification model using the Xception CNN model, a lightweight architecture that can be implemented on mobile devices. Our model attained an accuracy of roughly 76%, indicating the potential of face pictures and machine learning for the categorization of hair loss phases. Further research is required to improve the model’s performance and confirm its therapeutic value.

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Deep Learning Approach for Hair Loss Detection: Xception

  • 会议论文

  • 发表日期：

    2025年

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  • Shubh Ashish,
  • Garvit Sharma,
  • Gaurav Raj,
  • Ayan Sar,
  • Tanupriya Choudhury

摘要

Millions of individuals worldwide suffer from hair loss, which is a common and frequently distressing condition. Presently, dermatologists rely on visual assessments and subjective judgments to diagnose and classify hair loss. We present a novel approach to hair loss stage classification using facial pictures and machine learning algorithms in this research. We gathered a collection of facial photographs of people in various stages of hair loss and used computer vision algorithms to extract significant information from the images. We trained and evaluated a classification model using the Xception CNN model, a lightweight architecture that can be implemented on mobile devices. Our model attained an accuracy of roughly 76%, indicating the potential of face pictures and machine learning for the categorization of hair loss phases. Further research is required to improve the model’s performance and confirm its therapeutic value.