Convolutional Neural Networks for Non-Invasive Diagnosis of Androgenetic Alopecia using Dermoscopic Hair Images

Androgenetic alopecia, a common form of hair loss affecting both men and women, poses significant psychosocial and quality-of-life impacts. Current diagnostic methods, such as trichoscopy and biopsy, are often invasive, time-consuming, and subject to inter-observer variability. This research proposes a non-invasive approach to alopecia diagnosis using deep learning techniques applied to dermoscopic hair images. We develop a convolutional neural network (CNN) model tailored for automated detection and classification of androgenetic alopecia patterns from scalp image data. Through extensive experimentation on a curated dataset of dermoscopic hair images, we evaluate the performance of our CNN model against existing methods and explore interpretability techniques to enhance model transparency and trust. Saliency maps and feature attribution methods are employed to provide localized explanations and identify influential image regions for model predictions. Our findings demonstrate the potential of deep learning for accurate and scalable alopecia diagnosis, while emphasizing the importance of interpretability in clinical decision support. We discuss the implications, limitations, and future directions for integrating our approach into dermatological practice and telehealth applications.