A Convolutional Neural Network for Mammogram based Breast Cancer Detection

Breast cancer stands as the maximum widespread health concern affecting women globally. Mammograms serve as the primary diagnostic tool for detecting breast cancer. The proposed approach employs several steps to achieve early disease detection and extend individuals' lifespan. This study has proposed a model for breast cancer detection. Initially, mammograms undergo image preprocessing and are then given input into a model featuring eight convolutional layers and max-pooling layers to select the most pertinent features. Further, experimental evaluations will be conducted using two well-established benchmark datasets and then the model's performance has been assessed by confusion matrix parameters and yield improved results.