Deep Learning and CAD Systems in Breast Cancer Detection A Systematic Review of Diagnostic Accuracy and Clinical Integration
TL;DR
Deep learning-enhanced CAD systems represent a promising advancement in breast cancer diagnosis by improving diagnostic performance and supporting clinical decision-making, however, their successful implementation depends on high-quality data, transparent and interpretable models, and appropriate ethical and regulatory frameworks.
Abstract
Breast cancer continues to be a significant health challenge for women worldwide, and early detection is essential for improving survival outcomes. Over the years, Computer-Aided Diagnosis (CAD) systems have supported radiologists by highlighting suspicious regions in mammograms, ultrasound images, and magnetic resonance imaging (MRI). However, traditional CAD systems evaluate image features based on predefined rules and are associated with high false-positive rates, limiting their ability to adapt to new or complex cases. This study aimed to examine the role of deep learning (DL) in the development of computer-aided diagnosis (CAD) systems and to evaluate the diagnostic accuracy and clinical integration of AI-based approaches in breast cancer detection. A systematic review was conducted by analyzing 13 studies published between 2000 and 2024. Relevant studies were identified through a comprehensive search of the PubMed, IEEE Xplore, and Scopus databases. The included studies were evaluated according to their methodology, deep learning architectures including Convolutional Neural Networks (CNNs), ResNet, EfficientNet, and Vision Transformers and their reported diagnostic performance in terms of accuracy, sensitivity, specificity, and false-negative reduction. The findings were synthesized narratively to compare the advantages and limitations of AI-based diagnostic systems with traditional CAD systems. The findings demonstrate that DL-based models outperform traditional CAD systems. These models contribute to earlier diagnosis by detecting subtle lesions, microcalcifications, and asymmetrical structures that may be overlooked by the human eye. However, several challenges remain, including the availability of large, high-quality datasets, the interpretability of AI decisions, ethical considerations, and integration into routine clinical practice. Deep learning-enhanced CAD systems represent a promising advancement in breast cancer diagnosis by improving diagnostic performance and supporting clinical decision-making. However, their successful implementation depends on high-quality data, transparent and interpretable models, and appropriate ethical and regulatory frameworks to ensure that AI serves as a supportive tool that complements, rather than replaces, radiologists.