Abstract: This study presents a deep learning-based approach for automatic classification of scalp diseases and hair loss stages using image data. Leveraging convolutional neural networks (CNNs) with transfer learning, we evaluated multiple pre-trained models including ResNet50, VGG16, VGG19, and EfficientNet. Our method addresses challenges related to limited dataset size through image preprocessing and augmentation techniques, achieving high accuracy in distinguishing conditions like alopecia, psoriasis, and folliculitis, as well as hair loss progression stages. The trained models were integrated into a web application for user-friendly scalp condition diagnosis, enabling early detection and ongoing health monitoring.
Keywords: Deep Learning, Scalp disease, Hair Loss, Convolutional neural network (CNN).
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DOI:
10.17148/IJARCCE.2025.14621
