INTEGRATING DEEP NEURAL NETWORKS FOR RADIO PATHOLOGICAL CORRELATION IN EARLY CANCER DIAGNOSTICS: A MULTI-MODAL ANALYSIS

Abstract

Early cancer detection remains a central challenge in clinical oncology, largely due to the limitations of unimodal diagnostic approaches that consider radiological and histopathological data independently. This study presents a comprehensive multi-modal deep-learning framework designed to integrate radiology and pathology into a unified diagnostic system capable of identifying early-stage malignancies with higher accuracy and interpretability. Using paired CT, MRI, mammography, and whole-slide histopathology images, the model employs dual encoders and an attention-based fusion mechanism to learn cross-modal associations that reflect both macroscopic structural abnormalities and microscopic cellular transformations. Quantitative experiments demonstrate substantial improvements in classification performance, including notable gains in sensitivity, specificity, and prediction confidence compared with single-modality baselines. Qualitative assessments by radiologists and pathologists further validate that the system’s attention maps consistently highlight clinically meaningful regions, indicating strong alignment with established radiopathological reasoning. The model also showed robust generalizability across multiple cancer types, reinforcing the potential of multi-modal fusion to reduce diagnostic ambiguity and enhance early detection reliability. While challenges related to dataset heterogeneity and institutional variability persist, the findings confirm that radiopathological integration provides a highly promising pathway toward building advanced, clinically deployable AI-assisted screening tools. Overall, this research establishes multi-modal deep learning as a powerful strategy for bridging imaging and tissue-level diagnosis, ultimately supporting more timely and accurate clinical decision-making in early cancer diagnostics.