Tri-fusion deep learning model for early breast cancer screening using multimodal biomedical data
TL;DR
A Tri-Fusion deep learning model for timely breast cancer screening, which integrates White Blood Cell morphological features, mammogram image embeddings, and gene mutation signatures within a unified multimodal framework, exhibits strong generalization under cross-validation and robustness to the missing-modality scenario.
Abstract
To optimize the early detection of breast cancer, a strong integration of heterogeneous biomedical signals is essential to improve diagnostic reliability and reduce false negatives. This study proposes a Tri-Fusion deep learning model for timely breast cancer screening, which integrates White Blood Cell (WBC) morphological features, mammogram image embeddings, and gene mutation signatures within a unified multimodal framework. WBC features are extracted from peripheral blood smear images using a CNN-based morphologic encoder, mammographic representation is ensured by a pretrained deep convolutional foundation based on breast imaging information, and genomic details are modelled using a completely connected mutation-signature encoder deduced from a breast cancer–related gene panel. To capture cross-domain correlation, a dense categorization head is used for feature-level fusion. The proposed model uses three publicly available datasets: a WBC image dataset containing 12,500 samples, a CBIS-DDSM mammogram dataset containing 3,102 annotated instances, and a curated Genomic Dataset containing a mutant profile of 1200 patients from TCGA-BRCA. The experimental results show high accuracy compared with the unimodal and bimodal baselines, corresponding to 96.2% accuracy, 95.4% correctness, 94.8% recall, a F1-score of 95.1%, and an AUC-ROC of 98.6%. Furthermore, the model exhibits strong generalization under cross-validation and robustness to the missing-modality scenario.