Optimizing Breast Cancer Detection in Mammograms: A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification

Mammography, an X-ray-based imaging technique, plays a crucial role in the early detection of breast cancer. Its accuracy heavily depends on expert radiologists, making it essential to minimize interpretation errors. To support radiologists, various computer-aided detection and diagnostic methods have been proposed, increasingly leveraging advancements in artificial intelligence and machine learning. Over recent years, mammogram analysis has evolved significantly - from early patch-based classifiers, which examine only localized regions of images, to full-image classifiers, and later towards multi-view systems that simultaneously integrate multiple perspectives of the mammographic exam for enhanced accuracy. Despite this progression, critical questions remain, such as whether multi-view systems consistently outperform single-view approaches. In this paper, we systematically evaluate and compare the effectiveness of single-view and multi-view mammogram classification techniques. Our research introduces models that achieve superior performance relative to existing state-of-the-art approaches in both single-view and two-view classification scenarios. Furthermore, our findings provide valuable insights into optimal model architectures and effective transfer learning strategies, paving the way for more accurate and efficient mammogram interpretation. The inference code and model are available at https://github.com/dpetrini/multiple-view.