A Deep Learning Framework for Breast Cancer Detection Using Histopathological Imaging

Abstract

Breast cancer remains a significant global public health challenge, representing the most frequently diagnosed cancer among women and one of the leading causes of cancer-related mortality. Early detection and accurate diagnosis are critical for improving survival rates and expanding treatment options. Conventional diagnostic modalities such as mammography, ultrasound imaging, and biopsy are widely used; however, these approaches are often limited by inter-observer variability, image quality issues, and time-intensive clinical workflows. Recent advances in artificial intelligence, particularly deep learning, have demonstrated strong potential to support and enhance breast cancer detection through automated and consistent image analysis. This paper presents a comprehensive review of state-of-the-art deep learning techniques applied to breast cancer detection, with a focus on convolutional neural network–based approaches. In addition to the literature review, a practical implementation is presented to evaluate the effectiveness of deep learning in histopathological image classification. The proposed implementation employs the EfficientNetB3 architecture as a feature extraction backbone and is evaluated using the BreakHis dataset, which contains labeled breast histopathology images. The methodology includes image preprocessing, data augmentation, model training, and performance evaluation using standard validation metrics. Experimental results demonstrate that the proposed model achieves a validation accuracy of 97.4%, indicating strong discriminative capability and competitive performance relative to existing approaches reported in the literature. Despite these promising results, several challenges remain before widespread clinical adoption can be realized. These include ensuring model generalizability across diverse clinical settings, addressing data scarcity and class imbalance, improving interpretability to gain clinician trust, and navigating regulatory and ethical considerations. Overall, this study highlights both the progress and limitations of deep learning–based breast cancer detection systems and underscores their potential role as decision-support tools in future clinical workflows.