A Study of Images Based Skin Disease Recognition Using Deep Learning

Skin diseases, caused by bacteria, viruses, fungi, or allergies, are common and can range from minor irritations to severe conditions like skin cancer. Traditional diagnostic methods, such as laser-based systems, are often expensive and not widely accessible, particularly in low-resource settings. This study proposes a deep learning-based approach for the early detection and classification of skin diseases using image data. A dataset of approximately 8,000 dermatological images was curated, representing 10 common skin diseases and normal skin conditions in Bangladesh. The images were systematically preprocessed through normalization, scaling, and augmentation to ensure data quality. Multiple convolutional neural network (CNN) architectures-including DenseNet, NASNetMobile, MobileNetV2, EfficientNetB0, InceptionV3, VGG16, and VGG19-were evaluated using standard classification metrics: accuracy, precision, recall, and F1-score. Among these, DenseNet achieved the highest performance, with an accuracy of 91.87%, demonstrating superior capability in distinguishing between healthy and affected skin. The results confirm the potential of deep learning as a costeffective, scalable, and efficient tool for automated dermatological diagnosis, especially in regions with limited access to medical specialists.