Explainable Multi-Modal Deep Learning Framework for Tumor Progression Prediction and Survival Analysis

Tumor detection and classification using medical imaging have significantly advanced with the adoption of machine learning and deep learning techniques. Despite these improvements, predicting tumor progression and patient survival remains a challenging problem due to tumor heterogeneity, inter-patient variability, and limited longitudinal data. Accurate prognosis is essential for effective treatment planning and personalized healthcare. This paper proposes an explainable multi-modal deep learning framework for tumor progression prediction and survival analysis. The proposed approach integrates imaging data from MRI and CT scans with clinical information such as patient age, tumor stage, and treatment history. A hybrid architecture combining Convolutional Neural Networks (CNNs) and Transformer-based modules is employed to capture both spatial features and long-range dependencies. CNNs extract local structural details from medical images, while Transformers model temporal and contextual relationships associated with tumor progression.

To enhance interpretability, explainable AI techniques including SHAP and attention-based visualization are incorporated to identify key factors influencing model predictions. A survival analysis module is integrated to estimate patient-specific risk scores and progression timelines, enabling more personalized prognosis. The framework is evaluated on benchmark datasets using cross-validation, demonstrating improved accuracy, robustness, and generalization compared to conventional methods. The results highlight the effectiveness of combining multi-modal data and hybrid deep learning architectures for predictive modeling. Overall, the proposed system provides an interpretable and reliable solution for tumor progression prediction, supporting clinical decision-making and contributing toward patient-centered intelligent healthcare systems.