Skin Disease Detection Using Deep Learning

This project aims to identify skin disorders and solve important diagnosis-related issues resulting from their varied appearances and slight variations usually resembling one another. Traditional diagnosis relies heavily on the expertise of dermatologists, which can be limited by human subjectivity and resource availability. In this paper, we present a Convolutional Neural Network (CNN)-based deep learning method for automated identification of ten classifications of skin disease. The methodology involves preprocessing a dataset comprising high-resolution images of these conditions, followed by training a CNN architecture optimized for multi-class classification. Our CNN model leverages transfer learning techniques based on pretrained architectures such as ResNet and Densenet to extract hierarchical features from skin lesion images. This study employs data augmentation strategies to enhance model robustness and generalize unseen data. This proposed system aims to provide a reliable, efficient, and scalable tool for dermatologists and healthcare providers, aiding in early inaccurate diagnosis of skin diseases. Experimental results demonstrate promising accuracy rates in Densenet-121, indicating the potential for practical deployment in clinical settings to support medical decision-making and improve patient outcomes.