TY  - EJOU
AU  - Lim, Chee Chin 
AU  - Tiu, Hui Wen 
AU  - Oung, Qi Wei 
AU  - Lau, Chiew Chea 
AU  - Tan, Xiao Jian 

TI  - Advancing Breast Cancer Molecular Subtyping: A Comparative Study of Convolutional Neural Networks and Vision Transformers on Mammograms
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 86
IS  - 3
SN  - 1546-2226

AB  - Breast cancer remains one of the leading causes of cancer mortality world-wide, with accurate molecular subtyping is critical for guiding treatment and improving patient outcomes. Traditional molecular subtyping via immuno-histochemistry (IHC) test is invasive, time-consuming, and may not fully represent tumor heterogeneity. This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes. Four pretrained models, including two Convolutional Neural Networks (MobileNet_V3_Large and VGG-16) and two Vision Transformers (ViT_B_16 and ViT_Base_Patch16_Clip_224) were fine-tuned to classify images into HER2-enriched, Luminal, Normal-like, and Triple Negative subtypes. Hyperparameter tuning, including learning rate adjustment and layer freezing strategies, was applied to optimize performance. Among the evaluated models, ViT_Base_Patch16_Clip_224 achieved the highest test accuracy (94.44%), with equally high precision, recall, and F1-score of 0.94, demonstrating excellent generalization. MobileNet_V3_Large achieved the same accuracy but showed less training stability. In contrast, VGG-16 recorded the lowest performance, indicating a limitation in its generalizability for this classification task. The study also highlighted the superior performance of the Vision Transformer models over CNNs, particularly due to their ability to capture global contextual features and the benefit of CLIP-based pretraining in ViT_Base_Patch16_Clip_224. To enhance clinical applicability, a graphical user interface (GUI) named “BCMS Dx” was developed for streamlined subtype prediction. Deep learning applied to mammography has proven effective for accurate and non-invasive molecular subtyping. The proposed Vision Transformer-based model and supporting GUI offer a promising direction for augmenting diagnostic workflows, minimizing the need for invasive procedures, and advancing personalized breast cancer management.
KW  - Artificial intelligence; breast cancer; classification; convolutional neural network; deep learning; hyperparameter tuning; mammography; medical imaging; molecular subtypes; vision transformer

DO  - 10.32604/cmc.2025.070468