Hybrid deep learning-based model for covid-19 prediction and interpretation using multiple data modalities
| dc.contributor.author | Dokun, Oyewole | |
| dc.date.accessioned | 2024-11-18T11:28:45Z | |
| dc.date.available | 2024-11-18T11:28:45Z | |
| dc.date.issued | 2024-05 | |
| dc.description | The dissertation has tables and figures | |
| dc.description.abstract | 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. | |
| dc.description.sponsorship | Department of Information Management Technology, FUTO | |
| dc.identifier.citation | Dokun, O. (2024). Hybrid deep learning-based model for covid-19 prediction and interpretation using multiple data modalities (Unpublished Master's Thesis). Federal University of Technology, Owerri, Nigeria | |
| dc.identifier.uri | https://repository.futo.edu.ng/handle/20.500.14562/1506 | |
| dc.language.iso | en | |
| dc.publisher | Federal University of Technology, Owerri | |
| dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| dc.subject | Deep learning | |
| dc.subject | ML | |
| dc.subject | explainable AI | |
| dc.subject | classification | |
| dc.subject | covid-19 | |
| dc.subject | grad-cam | |
| dc.subject | Department of Information Management Technology | |
| dc.title | Hybrid deep learning-based model for covid-19 prediction and interpretation using multiple data modalities | |
| dc.type | Doctoral Thesis |