HYBRID AI FOR GASTRIC DIAGNOSTICS: MULTI-MODAL FUSION AND INTERPRETABILITY FOR EARLY ULCER AND CANCER DETECTION (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2025)
This study investigates the development of hybrid Artificial Intelligence (AI) models for the early and accurate diagnosis of gastric ulcer and gastric cancer. Traditional diagnostic methods such as endoscopy and biopsy, though widely used, face limitations including inter-observer variability, sampling errors, and delays in diagnosis. To address these challenges, two novel hybrid AI architectures are proposed: the Deep Feature-Attention SVM (DFA-SVM) and the Multi-Modal Fuzzy-Fusion Network (M2F2-Net). These models integrate multi-modal data sources, including endoscopic images, histopathology slides, and structured clinical records, while employing attention mechanisms, fuzzy logic, and interpretable AI approaches to enhance diagnostic performance. Experimental evaluation using the conceptual GIMIC-12K dataset demonstrates that M2F2-Net achieves superior performance, particularly in early gastric cancer detection, by significantly reducing false negatives and improving sensitivity. The findings highlight the transformative potential of hybrid AI in improving patient outcomes, optimizing healthcare efficiency, and supporting explainable decision-making in clinical practice.
Keywords: Hybrid Artificial Intelligence; Gastric Cancer; Gastric Ulcer; Multi-Modal Fusion; Deep Learning; Explainable AI (XAI); Diagnostic Imaging; Fuzzy Logic; Early Detection