CNN-KNN Model for Assessing Hair Health in Telogen Effluvium
February 2025
Studysummary This study documented that a CNN-KNN hybrid model achieved 98% accuracy in predicting hair breakage levels due to Telogen Effluvium, highlighting its potential for enhancing diagnosis and treatment in clinical dermatology through early detection of hair-related conditions.
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The study presents a CNN-KNN hybrid model designed to predict hair breakage levels in Telogen Effluvium, achieving an overall accuracy of 98% using a dataset of 10,050 images. The model effectively classifies hair damage across five Breakage Degrees, with BD2 showing precision, recall, and F1-score values of 94.20%. This high accuracy suggests the model's potential for clinical application in dermatological diagnostics and personalized hair care treatment planning. The research highlights the model's capability to enhance patient care through early detection and intervention of hair-related conditions, improving health outcomes and quality of life.
