Advancing Hair Disease Diagnostics: A Deep Learning Approach Using Inception-ResNet V2 for Multi-Class Classification
December 2024
New to Alopecia Areata? There is a guide in the encyclopedia. Read the guide → Studysummary This study developed a high-performance deep learning model using the Inception-ResNet v2 architecture to classify 10 hair disease classes, achieving an accuracy of 94.7% and balanced precision, recall, and F1-scores of 0.94, suggesting reliability for automated dermatology diagnostics.
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This study presents a deep learning model using the Inception-ResNet v2 architecture for classifying 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. While strong performances were noted for conditions like Folliculitis and Head Lice, some misclassifications occurred with overlapping conditions such as 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.
