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

This study introduces a deep learning framework using the ResNet50 model for automated classification of hair diseases, achieving a 95% accuracy on a dataset of 10 categories with 9600 training and 1200 validation images. The model demonstrated high true positive rates for conditions like Alopecia Areata and Lichen Planus, utilizing precision, recall, F1 score, and confusion matrix metrics. Techniques such as data augmentation and dropout were employed to enhance generalization. Despite minor misclassifications between similar conditions, the model effectively generalized to unseen data. Future directions include expanding the dataset, incorporating interpretability tools, and developing clinical applications, highlighting deep learning's potential to revolutionize dermatological diagnostics.