Optimizing Facial Skin Disease Classification using ResNet152V2 Architecture

When it comes to dermatology, one of the most important tasks is the categorization of face skin disorders. This classification has the ability to significantly improve both the diagnosis accuracy and the treatment results. The purpose of this research is to assess the effectiveness of three well-known convolutional neural network (CNN) architectures, namely ResNet152V2, VGG16, and MobileNet, in the classification of face skin disorders based on clinical photographs. Extensive trials were carried out with the use of a comprehensive dataset that included a variety of skin condition classifications. The classification performance of the ResNet152V2 model was found to be superior to that of both the VGG16 and MobileNet models. ResNet152V2's improved performance may be ascribed to its deeper design and better utilization of residual connections. These two factors help to alleviate the vanishing gradient problem and make it possible for the network to learn more complicated features.