Innovative Gabor Convolutional Networks for Accurate Breast Cancer Classification

Among women, breast cancer is one of the most common cancers, and it is a leading cause of cancer-related deaths. Early detection of breast cancer can greatly improve survival rates and enable timely medical intervention. Additionally, accurately distinguishing benign cases can prevent patients from undergoing unnecessary treatments. Consequently, the precise and early diagnoses of breast cancer, as well as the differentiation between benign and malignant tumors, are critical areas of research. In this paper, we propose a novel approach by designing a Gabor Convolutional Network (GCN) integrated with the XGBoost machine learning model. The performance of the proposed model is assessed using four evaluation metrics. Experimental results demonstrate that our model achieves a high detection accuracy of 99.79% in classifying breast cancer, indicating its effectiveness for breast cancer diagnosis.