Hybrid Deep Learning-Based Automatic Diagnosis of Breast Cancer from Mammograms Using Segmentation and Feature Selection

Medical image processing plays a crucial role in the early detection and classification of diseases, particularly breast cancer, which is a leading cause of death among women. Mammography is widely used for breast cancer diagnosis, but accurately interpreting mammograms remains challenging even for expert radiologists. To address this, we propose a novel framework for automatic breast cancer diagnosis that integrates mass segmentation, feature selection, and a hybrid deep learning-based classifier. Our approach introduces the enhanced black widow optimization (EBWO) algorithm for mass segmentation, effectively identifying the region of interest (ROI) in mammograms. The Improved UNet model is used for deep feature extraction, enhancing feature representations from the segmented masses. To tackle the issue of high-dimensional data, we employ the modified snow ablation optimization (MSAO) algorithm for optimal feature selection, ensuring the best features are chosen for classification. The optimal threshold graph neural network (OT-GNN) is then utilized for classifying breast cancer into three categories: normal, benign, and malignant. In comparison with existing methods, our framework demonstrates superior performance. We validate the proposed UNet+MSAO+OT-GNN method using augmented datasets such as DDSM and INbreast, achieving IOU scores of 99.912% and 99.909%, respectively. Our method outperforms existing classifiers, achieving accuracy scores of 99.523% and 99.859% on DDSM and INbreast datasets, respectively, showcasing significant improvements in both segmentation and classification accuracy. This highlights the effectiveness and novelty of our approach in comparison to traditional methods.