A Critical Analysis and Classification of Breast Cancer Using Histopathology Images

In Breast Cancer ML is instrumental in early detection through the analysis of mammographic and histopathological images, assessing individual risk factors, personalizing treatment plans based on genomic data, and providing decision support for healthcare professionals. ML also contributes to post-treatment monitoring, integrates diverse datasets for comprehensive insights, and accelerates research and drug discovery efforts. Overall, it enhances the accuracy, efficiency, and personalized nature of breast cancer care across the entire healthcare continuum. The paper presents a comparative review of recent techniques that use histopathological images for breast cancer classification. The study synthesizes findings from a range of research efforts employing diverse methodologies, including deep learning, ensemble techniques, and innovative model architectures. Researchers have explored the application of artificial neural networks, convolutional neural networks, and transfer learning in increasing the accuracy and objectivity of breast cancer classification. Additionally, novel approaches such as ViT-AMCNet, Ensembled Transfer Learning (ETL), and Curriculum Feature Alignment Network (CFAN) have been introduced, each contributing to the evolving landscape of histopathology image analysis. This review also includes a case study based on the EfficientNetB3 architecture that achieved a $98 \%$ accuracy in the diagnosis of breast cancer, with remarkable recall values of 0.95 and 0.97 for positive and negative cases, respectively, along with precision rates of 0.96 for positive cases and $\mathbf{0. 9 5}$ for negative cases. The review highlights the significance of these approaches in addressing challenges related to feature extraction, nuclei segmentation, and classification accuracy, ultimately advancing our understanding of breast cancer pathology through the lens of computational methodologies.