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Hybrid deep learning-based model for covid-19 prediction and interpretation using multiple data modalities
(Federal University of Technology, Owerri, 2024-05) Dokun, Oyewole
This research addresses the critical need for accurate and timely COVID-19 diagnosis and prognosis by developing a hybrid deep learning model that integrates multiple data modalities, including chest X-rays, Computed Tomography (CT) scans, blood smears, and clinical data. The model employs specialized architectures such as Residual Network with 50 Layers (ResNet50) for Chest X-ray, InceptionV3 for CT scans, Convolutional Neural Network (CNN) for blood smears, and a Random Forest classifier for clinical data analysis. The results demonstrate high accuracy rates: 96.7% for ResNet50, 97.58% for InceptionV3, 96.12% for CNN, and 98.30% for the Random Forest classifier. Grad-CAM enhances transparency by visualizing critical regions in the images, aiding healthcare professionals in understanding the model's decisions. This hybrid model offers improved accuracy and reliability for COVID-19 diagnosis and prognosis, making it a valuable tool for clinical settings and resource allocation. The research underscores the potential of multi-modal data integration in medical AI and suggests further exploration and refinement of such models for broader healthcare applications.