DEVELOPMENT OF A CNN-BASED DEEP LEARNING MODEL FOR BREAST CANCER SCREENING AND DIAGNOSIS

Breast cancer remains one of the leading causes of mortality among women worldwide, where early detection and accurate classification are pivotal for effective treatment and improved survival rates.This research presents a novel Convolutional Neural Network (CNN)-based deep learning framework for the automated detection and multi-class classification of breast cancer using mammogram images.The proposed architecture is designed to extract hierarchical features from the input mammograms and classify them into normal, benign, and malignant categories with high precision.The system is trained and evaluated on the publicly available Digital Database for Screening Mammography (DDSM), which provides high-quality grayscale mammogram images.A comprehensive preprocessing pipeline including image resizing, normalization, and augmentation was implemented to enhance model generalization.The CNN architecture comprises three convolutional blocks followed by