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

This research explores a non-invasive method for diagnosing androgenetic alopecia using convolutional neural networks (CNNs) applied to dermoscopic hair images. The study highlights the limitations of current diagnostic methods, such as trichoscopy and biopsies, which are invasive and subject to variability. The CNN model developed in this study aims to automate the detection and classification of alopecia patterns, demonstrating potential for accurate and scalable diagnosis. The research emphasizes the importance of model interpretability, using saliency maps and feature attribution to provide localized explanations for predictions. The findings suggest that deep learning could enhance clinical decision support in dermatology and telehealth, although further integration into practice is needed.