eDermaCare: Enhancing Skin Disease Classification with ResNet50

In today's world, skin diseases are caused by various factors and affect a person's mental and physical wellbeing. Particularly in private areas, there is considerable reluctance to disclose skin diseases, especially among female patients, which further intensifies the challenge of early detection and treatment. To tackle this issue, the study proposes the implementation of eDermaCare, a novel approach leveraging the advanced Residual Network-50 (ResNet-50) convolutional neural network architecture. Several convolutional neural network (CNN) architectures were explored, but unsatisfactory results were obtained. ResNet-50 has the ability to capture complex features from skin images. The goal is to improve skin disease classification using ResNet-50's deep learning capabilities. This model uses the rest of the course to explain training and retention issues. Block balances allow training deep networks without suffering from degradation in performance. As a result, the model achieves a classification accuracy of 76.07%. The proposed system wm help improve early detection and treatment of skin diseases, address societal challenges, and support effective healthcare.