Xception Convolutional Deep Maxout Network for Enhanced Breast Cancer Classification Using Histopathological Images

Globally, breast cancer represents the leading cancer type, with millions of women impacted annually. The success of breast cancer treatment relies heavily on timely detection and precise tumor classification. The classification of breast cancer has gained considerable importance in Deep Learning (DL) and medical research with the development of medical imaging techniques, like histopathological imaging. Many existing DL schemes suffer from overfitting and endure difficulties in effectively mining the key features from high-resolution images with subtle variations. Hence, the Xception Convolutional Deep Maxout Network (Xcov-DMN) is developed to classify breast cancer. At the initial stage, the Mean-Shift Filter is applied to the input histopathological image. Following this, the White Blood Cell Network (WBC-Net) is employed for blood cell segmentation with the Balanced Cross-Entropy (BCE) and Focal Loss for ensuring precise segmentation. Next, Colored Histograms, shape features, Haralick Texture Features, and Complete Local Binary Pattern (CLBP) features are excerpted. Consequently, the developed Xcov-DMN is utilized to classify breast cancer. Xcov-DMN is the combination of the Deep Maxout Network (DMN), Fractional Calculus (FC), and Xception Convolutional Neural Network (XCovNet). Moreover, with learning data at 90%, the Xcov-DMN achieved the highest accuracy of 92.755%, True Negative Rate (TNR) of 91.977%, and True Positive Rate (TPR) of 94.765%.