Breast Cancer Image Classification into Benign and Malignant using an Intelligent CNN Framework

Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide. Accurate diagnosis from histopathological biopsy samples is essential, yet manual examination is time-consuming and subject to inter-observer variability, particularly given the shortage of trained pathologists alongside the increasing number of cases. Deep learning, especially Convolutional Neural Networks (CNNs), has emerged as a powerful tool for classifying medical images by automatically extracting discriminative features from raw data. In this study, we investigate the use of the publicly available Breast Cancer Histopathological (BreakHis) image database, which contains benign and malignant samples across multiple magnifications. Our proposed approach extracts isolated image patches, applies CNN-based feature learning and integrates multi-resolution information to improve classification performance. To enhance generalization under limited data, we adopt transfer learning with optimized fine-tuning. Experiments implemented in MATLAB demonstrate that our CNN-based framework achieves higher accuracy than traditional machine learning approaches relying on handcrafted texture descriptors. These findings highlight the potential of CNNs, combined with patch-based multi-resolution analysis, to support pathologists in reliable and efficient breast cancer diagnosis.