Published Paper Details:

Skin diseases, which affect millions of individuals worldwide, pose a significant health risk if not diagnosed and treated promptly. Early detection of skin diseases such as melanoma, eczema, and psoriasis is crucial for improving patient outcomes. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have shown remarkable promise in automating the diagnosis of skin diseases through image analysis. This paper presents a CNN-based approach to detect and classify various skin conditions from clinical images. The proposed system uses a deep convolutional architecture to extract meaningful features from high-resolution skin images and classify them into different categories. The model is trained on a large dataset of labeled images, incorporating both dermatological knowledge and neural network capabilities. The results demonstrate that the CNN model achieves high accuracy and efficiency in detecting skin diseases, outperforming traditional image processing techniques and providing a potential tool for assisting dermatologists in clinical settings