Skin Disease Detection System Using Convolutional Neural Network

The "Skin Disease Detection System Using Convolutional Neural Network" is designed to accurately classify various skin diseases through advanced image processing techniques. The process begins with the acquisition of input images, followed by several pre-processing steps to enhance image quality. Augmentation techniques such as rotation, flipping, and zooming are applied to increase the diversity of the training dataset and improve the model's robustness. The core of the system is a carefully designed Convolutional Neural Network (CNN) architecture, optimized for skin disease classification. The dataset is split into training, validation, and testing sets, with approximately 70-80% allocated for training and 10-15% for validation. This ensures a well-rounded model capable of generalizing to new data. The final classification step involves identifying specific skin diseases, including Actinic keratosis, Dermatofibroma, Melanoma, and Squamous cell carcinoma. This system aims to assist dermatologists in early and accurate diagnosis, ultimately improving patient outcomes through timely and precise treatment.

Keywords: Skin Disease Dataset, Image Processing Techniques, Deep Learning Techniques, Convolution Neural Network, Classification, Accuracy.