Skin Disease Detector using CNN

Skin conditions are common and frequently need for a quick and precise diagnosis in order to be treated. In this paper, we propose a Convolutional Neural Network (CNN) based skin disease detection system. CNNs are ideally suited for the identification of skin diseases from photos because they have shown impressive effectiveness in image classification tasks. We make use of a sizable collection of skin picture annotations that span a wide variety of dermatological disorders. Multiple convolutional and pooling layers are used in our CNN architecture to automatically extract discriminative features from input photos. Using a variety of supervised learning strategies, we train the CNN model to maximize performance measures including accuracy, precision, recall, and F1-score. By means of comprehensive testing and analysis, we exhibit the efficacy of our CNN-based skin disease detector in precisely recognizing diverse skin