Detection of Skin Cancer using MATLAB

Early identification is crucial for improving treatment outcomes and survival rates for skin cancer, which is a major worldwide health concern. This study uses MATLAB as the main programming platform to create a reliable deep learning-based system for skin cancer detection. The suggested method consists of many steps: automated feature extraction using Convolutional Neural Networks (CNNs), categorization into benign or malignant categories, and picture preparation to improve dermoscopic image quality. A dataset of annotated dermoscopic images is used to train and verify the model, guaranteeing a varied representation of skin lesions to enhance generalization. According to experimental data, the suggested CNN-based model is effective in automated skin cancer diagnosis, with a classification accuracy of 78%, precision of 76.5%, and recall of 79.2%. By utilizing deep learning, this technique lessens reliance on human interpretation, helping medical practitioners identify skin cancer early. According to the results, CNN-based models have the potential to improve dermatological diagnostics by providing a dependable, scalable, and affordable option for practical clinical applications. Future research will concentrate on enhancing real-time diagnostic capabilities, expanding the dataset size, and optimizing the model architecture.