Deep Learning-Based Detection of Hair and Scalp Diseases Using CNN and Image Processing

Hair and scalp disorders affect millions worldwide, often undiagnosed in their early stages due to similarities with normal hair shedding. Conditions such as alopecia, psoriasis, and folliculitis require professional dermatological evaluation, which can be time-consuming and lead to delayed treatment. Early diagnosis and clinical decision-making have been strengthened by automating disease detection in healthcare through the development of deep learning and improved image-processing techniques. This study evaluated the three primary conditions associated with the scalp using a convolutional neural network (CNN) model. The research faced challenges due to the restricted availability of structured datasets and variations in image quality from several sources. To address this, 150 images were gathered from various repositories and subjected to pre-processing techniques, including denoising, contrast enhancement, image equalization, and data balancing. The processed dataset was employed to train a 2D CNN model with a training and validation accuracy of 97% and 92%. Additionally, we curated a dataset of scalp images to support further research in this domain, promoting the development of more robust automated diagnostic systems.