Breast Cancer Image Screening Classification Based on Convolutional Neural Networks

Abstract

Breast cancer is a prevalent form of cancer, especially among women, and can spread to other parts of the body if not detected early. Early diagnosis is vital for improving survival rates and the effectiveness of treatment, but this process is complex and demands considerable time and expertise from pathologists. To address these challenges, computer-aided methods have become increasingly important in analyzing histopathological images for breast cancer detection. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown significant potential in this area. Unlike traditional methods that focus on low-level feature extraction, CNNs can learn high-level abstract features from images. By training a CNN on a dataset of labeled histopathological images, it learns to identify cancer-specific features, such as abnormal cell structures and patterns. Once trained, the CNN can accurately classify new images as either cancerous or non-cancerous, providing valuable assistance to pathologists. This not only speeds up the diagnostic process but also enhances the accuracy of early breast cancer detection, leading to more timely and effective treatment. Deep learning and CNNs thus play a crucial role in improving outcomes for breast cancer patients by aiding in the early and precise diagnosis of the disease.