Deep Learning Based Breast Cancer Detection Using Prababilistic Neural Network

Breast cancer is a major health concern affecting millions of women worldwide. Early detection greatly enhances survival rates and reduces the burden of treatment. This research presents a Computer-Aided Diagnosis (CAD) system that uses advanced image processing and deep learning techniques to classify mammographic images into benign or malignant categories. The system incorporates preprocessing, segmentation using Gaussian Mixture Models (GMM), feature extraction with Gray Level Co-occurrence Matrix (GLCM), and classification using a Probabilistic Neural Network (PNN). Developed in MATLAB and tested on the mini-MIAS database, the system achieved an accuracy of 99.4%, sensitivity of 99.3%, and specificity of 100%. This paper emphasizes the potential of automated systems in supporting medical diagnosis and enabling early intervention.