TransBreastNet a CNN transformer hybrid deep learning framework for breast cancer subtype classification and temporal lesion progression analysis

Breast cancer continues to be a global public health challenge. An early and precise diagnosis is crucial for improving prognosis and efficacy. While deep learning (DL) methods have shown promising advances in breast cancer classification from mammogram images, most existing DL models remain static, single-view image-based, and overlook the longitudinal progression of lesions and patient-specific clinical context. Moreover, the majority of models also limited their clinical usability by designing tests for subtype classification in isolation (i.e., not predicting disease stages simultaneously). This paper introduces BreastXploreAI, a simple yet powerful multimodal, multitask deep learning framework for breast cancer diagnosis to fill these gaps. TransBreastNet, a hybrid architecture that combines convolutional neural networks (CNNs) for spatial encoding of lesions, a Transformer-based modular approach for temporal encoding of lesions, and dense metadata encoders for fusion of patient-specific clinical information, forms the backbone of our system. The breast cancer subtype and disease stage are predicted simultaneously from a dual-head classifier. They are then used to construct temporal lesion sequences, either by employing genuine longitudinal data or by adding sequence augmentation to sample sequences, thereby strengthening the model's ability to learn Progression Patterns. We conduct extensive experiments on a public mammogram dataset and demonstrate that our model outperforms several state-of-the-art baselines in both subtype classification, achieving a macro accuracy of 95.2%, and stage Prediction, with a macro accuracy of 93.8%. We also provide ablation studies, which confirm how every module contributes to the framework. Unlike prior static single-view models, our framework jointly models spatial, temporal, and clinical features using a CNN-Transformer hybrid design. It simultaneously predicts breast cancer subtypes and lesion progression stages, while generating synthetic temporal lesion sequences where longitudinal data is scarce. Built-in explainability modules enhance interpretability and clinical trust. BreastXploreAI offers a robust, scalable, and clinically relevant approach to diagnosing breast cancer from full-field digital mammogram (FFDM) images. ZH is computationally capable of analyzing spatial, temporal, and clinical features simultaneously, which enables a more informed diagnosis and lays the foundation for improved clinical decision support systems in oncology.