Development and Validation of a Diagnosis System for Lung Infection Using Hybrid Deep-Learning Techniques
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Abstract
A fast and accurate test is necessary to detect COVID-19. A computed tomography (CT) scan has shown diagnostic accuracy. CT scan processing using a deep learning architecture may improve illness diagnosis and treatment. We proposed a deep learning system for COVID-19 detection using CT images, including using and comparing transfer-learning, fine-tuning, and the embedding process. This paper presents the development of a COVID-19 case identification model using deep learning techniques. The suggested model utilized a modified visual geometry group (VGG16) architecture as the deep learning framework. The model was trained and validated using a chest CT image dataset. The SARS-COV-2 dataset contains 2482 CT scans of 210 patients from publicly available sources. The modified model demonstrated encouraging outcomes by greatly enhancing the sensitivity measure (95.82±1.75)%, which is an essential criterion for accurately detecting instances of COVID-19 infection. In addition, the model achieved higher values for the accuracy metric (91.67±1.68)%, the specificity meter (88.08±3.72)%, the precision metric (87.51±3.27)%, the F1 score (91.43±1.55)%, and the area under the curve (91.98±1.55)%. Deep learning effectively detects COVID-19 in chest CT scan images. Clinical practitioners may employ the suggested approach to study, identify, and effectively mitigate a greater number of pandemics.
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Journal of Studies in Science and Engineering is licensed under a Creative Commons Attribution License 4.0 (CC BY-4.0).
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