Advanced Deep Learning Framework for Early Breast Cancer Detection with Graph Convolutional Networks

Women with breast cancer are very common. Early signs are rare; hence the condition is often identified late. Later-stage treatment outcomes are less predictable and harder. Complementary screening programs and preventative checkups have considerably improved early breast cancer identification. Reducing the number of unscreened women and increasing early-stage breast cancer detection demands better screening methods. Iterative model training included preprocessing, segmentation, feature extraction, and detection. Preprocessing was necessary for well-read results. Corners were identified for segmentation using Shi-Tomasi. Feature extraction uses SIFT. Without modifying photo size, this approach discovers similar photos. The research models were all GCN-trained. CNNs and SVMs were employed to evaluate the GCN-based model. GCN's mean accuracy of 92.34 percent beat the competition. This shows that the GCN-based technique improves breast cancer diagnosis. Due to early discovery and treatment, this strategy may improve patient outcomes.