Mammography images: Visual Geometric Group (VGG) model and customization for breast cancer detection
M. Shanmugapriya, J. Pradeepkandhasamy · 2025
Breast Cancer is the most deadly disease for women worldwide. Breast Cancers fall into two categories: benign, which causes less harm, and malignant, which causes great danger. Early detection of breast cancer can reduce mortality and improve recovery. Researchers worldwide are currently focusing on creating Medical imaging is used to detect Breast Cancer. Deep learning methods have garnered significant interest from researchers in the medical imaging domain owing to their rapid advancements. In this study, mammography images were employed to identify Breast Cancer utilizing the Visual Geometric Group (VGG) technique. VGG, a collection of Convolutional Neural Network (CNN) architectures, is renowned for its profoundness and straightforwardness. VGG models have exhibited noteworthy execution across a scope of PC vision errands, like picture order, object recognition, and semantic division. The VGG model has been tailored specifically this study aims to detect Breast Cancer. A new classification layer is incorporated to modify the architecture of a VGG model. The data sets utilized for our experiment are sourced from two distinct repositories, namely CBIS-DDSM and INbreast are two databases for breast imaging. The customized VGG model achieves an accuracy of 88%. The computer-assisted analysis assists radiologists in improving the accuracy of breast cancer diagnoses.