An Explainable Generative Ai-driven Multimodal Deep Learning Framework For Kidney Tumor Localization And Classification Using Ct And Mri

Abstract

Kidney Tumors Are A Major Clinical Concern In Which Early Detection, Accurate Localization, And Reliable Classification Are Essential For Effective Treatment Planning And Improved Patient Outcomes. Computed Tomography (CT) And Magnetic Resonance Imaging (MRI) Provide Complementary Anatomical And Tissue Information; However, Their Interpretation Is Time-consuming And Highly Dependent On Expert Radiologists, Which May Lead To Inter-observer Variability And Diagnostic Errors. To Address These Challenges, This Research Proposes An Explainable Generative-AI-driven Multimodal Deep Learning Framework For Automated Kidney Tumor Analysis. The Proposed System Integrates CT And MRI Images Through Preprocessing And Multimodal Registration, Followed By Generative Adversarial Network (GAN)-based Synthetic Data Augmentation To Overcome Limited Medical Imaging Data And Improve Model Generalization. An Attention-based Segmentation Network Is Employed To Localize Kidney Tumor Regions, While A Hybrid CNN–Transformer Architecture Extracts Complementary Local And Global Representations From The Multimodal Images. CNNs Capture Fine-grained Spatial And Textural Characteristics, Whereas Transformer-based Learning Models Long-range Contextual Dependencies. A Cross-Modal Attention Mechanism Is Further Proposed To Learn Interactions Between CT And MRI Representations, Followed By An Adaptive Multimodal Attention Feature Fusion (AM-AFF) Mechanism That Dynamically Weights And Integrates The Learned Features For Robust Tumor Classification. To Enhance Clinical Transparency, Explainable Artificial Intelligence (XAI) Techniques Are Incorporated To Visualize The Regions Contributing To The Model's Predictions. The Proposed Framework Is Designed To Provide Automated Tumor Localization And Classification While Improving Robustness, Multimodal Feature Representation, Interpretability, And Diagnostic Consistency. The Resulting System Aims To Serve As An Intelligent Clinical Decision-support Framework For Assisting Healthcare Professionals In Accurate And Efficient Kidney Tumor Assessment.

Keywords