Multi-class Classification of Breast Digital Pathology Image

In 2020, almost 1 in 4 of the newly diagnosed female cancer cases were breast cancer, and nearly 700,000 women died of breast cancer worldwide, accounting for 15.5% of female mortality.1 Therefore, early diagnosis of breast cancer plays a significant role in reducing cancer-related deaths. In clinical practice, breast cancer is initially diagnosed by physical examination and medical imaging, including bilateral breast palpation, mammography, and ultrasonography. Materials and MethodsA Deep Learning Framework for Multi-Class ClassificationIn our study, we proposed a deep learning approach designed for the analysis of breast cancer. The classification of breast digital pathology images must rely on morphology-related features such as the density and variability of the nucleus. Understanding the evolution of the disease is critical for the prevention and treatment of breast cancer, especially for early diagnosis.