Advancing Hair Disease Diagnostics: A Deep Learning Approach Using Inception-ResNet V2 for Multi-Class Classification

This study developed a deep learning model using the Inception-ResNet v2 architecture to classify 10 hair disease classes, including Alopecia Areata and Male Pattern Baldness, with a dataset of 12,000 images. The model achieved a high accuracy of 94.7% and demonstrated balanced precision, recall, and F1-scores of 0.94 for each class. Strong performances were noted for Folliculitis and Head Lice, though some misclassifications occurred with overlapping conditions like Male Pattern Baldness and Seborrheic Dermatitis. The research highlights the potential of deep learning in dermatological diagnostics and suggests further work to expand datasets and improve model interpretability for clinical application.