Comparative Analysis of Deep Learning Models for Breast Cancer Classification on Multimodal Data

Rising breast cancer incidence and mortality represent significant global challenges for women. Deep learning has demonstrated superior diagnostic performance in breast cancer classification compared to human experts. However, most deep learning methods have relied on unimodal features, potentially limiting the performance of diagnostic models. Additionally, most studies conducted so far have used a single view of digital mammograms, which significantly reduces model performance due to limited overall perspective and generalizability.