Abstract

Skin cancer disease detection and analysis heavily rely on human visual inspection, limited by microscopic behavior. Computer-based image recognition systems have been instrumental in achieving accurate classification and identification of skin cancer diseases. This research incorporates K-means clustering for real-time skin lesion image detection, followed by feature extraction through Gray Level Co-occurrence Matrix (GLCM)-based texture features, Discrete Wavelet Transform (DWT)-based low-level features, and Statistical Color features. While classification is typically conducted using SVM-based methods, these are less accurate with respect to texture features. To improve feature-based matching, an advanced artificial intelligence approach based on Probabilistic Neural Networks (PNN) is introduced for classification. The proposed methodology is implemented in the MATLAB environment and demonstrates significantly better accuracy compared to conventional approaches.

Description