Fibroglandular Tissue Classification in Breast Mri: A Comparative Study of Automated Decision Strategies Publisher



Khalaj M ; Arian A ; Torabi A ; Ahmadinejad N ; Gity M ; Yazdi S N M ; Afshari M P ; Tabrizi M S ; Soltanian Zadeh H
Source: 2026 International Interdisciplinary Conference on Artificial Intelligence: Engineering, Health, Finance and Humanities, IICAI 2026 Published:2026

Abstract

Fibroglandular tissue (FGT) assessment in breast magnetic resonance imaging (MRI) is a key factor in breast cancer risk evaluation and follows the BI-RADS lexicon standard. Most automated methods have focused on segmentation, while classification-based approaches remain limited. Previous studies have often analyzed both breasts together, overlooking BI-RADS recommendations for side-specific evaluation and alternative decision strategies. This study investigates three assessment methods for automated FGT classification: the conventional BI-RADS Maximum Rule, a Probability Averaging Rule for bilateral integration, and a Lower-Uncertainty Rule that uses Shannon entropy to prioritize more confident predictions. These strategies were tested using three deep learning architectures, including MobileNetV2, ResNeXt-26, and a hybrid ViT-ResNet model, on 654 precontrast T1-weighted breast MRI scans. Across ten independent runs, the ViT-ResNet model with the Probability Averaging Rule achieved the highest performance, with an accuracy of 0.85, an F1 score of 0.84, and a Cohen's kappa of 0.78. Both proposed strategies surpassed the conventional rule, and the expert-annotated dataset is publicly released to enable reproducible research. © 2026 IEEE.