A GAN-Enhanced Multimodal Diagnostic Framework Utilizing an Ensemble of BiLSTM, BiGRU, and RNN Models for Malaria and Dengue Detection

Abstract

Quick detection of Malaria and Dengue is crucial for doctors to start treatment and manage patients effectively. As patient conditions become more complex with overlapping symptoms, traditional diagnostic tools become inefficient, slow, and less accurate. Modernizing diagnostics with AI-powered systems is essential. Inaccurate or delayed diagnoses lead to transmission and sustained spread of these diseases. Improving diagnostic tools with accuracy, precision, recall, and speed enhances patient outcomes, reduces infection spread, and streamlines health sector operations. Despite advances, current diagnostic algorithms have weaknesses, especially in applying machine learning to diverse datasets at granular levels. Continuous effort is needed to improve accuracy and recall. This research proposes a GAN-Based Synthesized Multimodal Diagnostic System, combining BiLSTM, BiGRU, and RNN approaches. Utilizing GANs for data augmentation and recurrent networks, this framework shows innovative infectious disease detection. It improves diagnostic precision by 4.9%, accuracy by 3.5%, recall by 3.5%, and AUC by 4.5%, while reducing the gap between disease progression and detection by 8.3%. These outcomes can reduce triage time, misdiagnoses, and lead to faster, quality healthcare. The GAN-Enhanced Multimodal Diagnostic Framework shows promise for diagnosing Malaria, Dengue, and other infectious diseases.