Skin Disease Detection Using Deep Learning.

Abstract

For successful treatment and better patient outcomes, skin disorders must be identified early and accurately. Conventional diagnostic techniques, which mostly depend on dermatologists' visual inspection, are frequently arbitrary and based on the expertise of the practitioner. The application of deep learning models such as CNN, VGG16, MobilenetV2, and Densenet121 for automated skin disease identification from dermatoscopic pictures is investigated in this work. Our methodology allows the models to recognize complex patterns and characteristics typical of different skin disorders. We overcome the difficulties caused by sparse and unbalanced datasets by applying transfer learning and data augmentation strategies, guaranteeing strong model performance across several skin disease categories. When tested on a small picture dataset, the suggested CNN-based system outperforms conventional machine learning techniques in terms of accuracy, sensitivity, and specificity. Furthermore, the model offers graphical explanations to support its predictions, improving interpretability and building medical experts' confidence. According to the findings, deep learning may greatly enhance the early diagnosis and screening of skin conditions, providing a trustworthy instrument for initial screening and diagnosis. Because it makes fast and accurate therapeutic interventions possible, this development has the potential to save healthcare expenditures while also improving patient care. We demonstrate the efficiency of our technique by correctly and robustly classifying a broad spectrum of skin illnesses through a comprehensive performance evaluation