Breast Cancer Histopathological Image Analysis Using deep learning

Breast cancer is a major global health issue and a leading cause of cancer-related deaths among women, making early and accurate diagnosis essential for improving survival rates. Histopathological examination of breast tissue is the gold standard for detection, but this manual process is time-consuming, highly dependent on expert pathologists, and prone to inter-observer variation. To address these limitations, this project aims to develop an automated breast cancer classification system using Deep Learning, specifically Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). The BreakHis dataset, containing high-resolution histopathological images categorized as benign or malignant, will be used for training and evaluation. The workflow includes image preprocessing, data augmentation (rotation, flipping, zooming), model design, training, validation, and performance assessment using accuracy, precision, recall, and F1-score. A comparative analysis between CNN and SVM models will highlight their effectiveness and potential for real-world medical use. The proposed system is designed as a computer-aided diagnosis (CAD) tool to assist pathologists by reducing workload, improving diagnostic consistency, and accelerating detection. With strong interpretability and reliability, the model aims to deliver high-confidence cancer predictions and can be extended to multi-class classification and other cancer types in the future.