Development of a Cost-Effective Novel CNN Model for Breast Cancer Classification in Histopathological Images

Cancer, is a leading cause of disease and death worldwide, affecting both advanced industrialized and developing nations. Breast cancer, specifically among women, is a highly prevalent and serious type of cancer, making it a focal point for extensive research in the field of medicine. In the diagnosis of breast cancer, histopathological images play a crucial role because of the abundance of valuable phenotypic information they contain. To enhance the accuracy and objectivity of breast histopathological image analysis (BHIA), classification, and detection tasks are performed on these images using deep learning architecture approaches. In a preliminary experiment conducted in this paper using the Breast Cancer Histopathological Database (BreakHis), four state-of-the-art and custom CNN architectures were proposed. The experimental results demonstrate the notable performance of the proposed custom model at 40x and 200x magnification factors, reaching accuracies of 97.49% and 97.77%, surpassing other models. The ResNet-50 model achieved higher accuracy at 100x and 400x magnifications, with accuracies of 98.56% and 96.43%, respectively. Compared to other state-of-the-art models, the proposed CNN model not only shows efficient training with a significantly shorter timeframe but also features a reduced number of layers, highlighting its superior computational efficiency. Although the parameter count is higher than that of one of the models, the model strikes a favorable balance between computational efficiency and model capacity. In light of the achieved outcomes and the existing literature, forthcoming studies endeavor can be pursued further to enhance the performance values in breast cancer classification.