Deep Learning-Powered Hair Disease Diagnosis: A ResNet50 Approach for Scalable and Accurate Classification

    January 2025
    Pratham Kaushik, Sunila Choudhary
    Studysummary This study found that a deep learning framework using the ResNet50 model achieved 95% overall accuracy in classifying 10 categories of hair diseases, demonstrating reliable performance but also identifying potential improvements due to misclassifications between similar conditions.
    Automatically generated from the study's abstract, not written by a person, and not a review of the full paper. Not medical advice or a treatment recommendation. Read the original study, and consult a qualified healthcare professional before changing treatment. Full disclaimer
    Read the full study on ieeexplore.ieee.org →
    Discuss this study in the Community →

    Research cited in this study 7

    1. Comparative Studies of Hair Shaft Components Between Healthy and Diseased Donors PloS one · 2024
    2. Leveraging Deep Neural Networks to Uncover Unprecedented Levels of Precision in the Diagnosis of Hair and Scalp Disorders Skin research and technology · 2024
    3. Enhanced Hair Disease Classification Using Deep Learning 2024
    4. Methods of Transfer Learning for Multiclass Hair Disease Categorization 2023
    5. Hair and Scalp Disease Detection Using Machine Learning and Image Processing European Journal of Information Technologies and Computer Science · 2023
    6. Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks Sensors · 2022
    7. ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health IEEE Access · 2020