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Fully automated unified prognosis of Covid-19 chest X-ray/CT scan images using Deep Covix-Net model

Vinod, Dasari Naga, Balasundaram, Rebecca, Zungeru, Adamu Murtala and Prabaharan, S.R.S. (2021) Fully automated unified prognosis of Covid-19 chest X-ray/CT scan images using Deep Covix-Net model. Computers in Biology and Medicine, 136. p. 104729.

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SARS-COV2 (Covid-19) prevails in the form of multiple mutant variants causing pandemic situations around the world. Thus, medical diagnosis is not accurate. Although several clinical diagnostic methodologies have been introduced hitherto, chest X-ray and computed tomography (CT) imaging techniques complement the analytical methods (for instance, RT-PCR) to a certain extent. In this context, we demonstrate a novel framework by employing various image segmentation models to leverage the available image databases (9000 chest X-ray images and 6000 CT scan images). The proposed methodology is expected to assist in the prognosis of Covid-19-infected individuals through examination of chest X-rays and CT scans of images using the Deep Covix-Net model for identifying novel coronavirus-infected patients effectively and efficiently. The slice of the precision score is analysed in terms of performance metrics such as accuracy, the confusion matrix, and the receiver operating characteristic curve. The result leans on the database obtainable in the GitHub and Kaggle repository, conforming to their endorsed chest X-ray and CT images. The classification performances of various algorithms were examined for a test set with 1800 images. The proposed model achieved a 96.8% multiple-classification accuracy among Covid-19, normal, and pneumonia chest X-ray databases. Moreover, it attained a 97% accuracy among Covid-19 and normal CT scan images. Thus, the proposed mechanism achieves the rigorousness associated with the machine learning technique, providing rapid outcomes for both training and testing datasets.

Item Type: Article
Status: Published
DOI: https://doi.org/10.1016/j.compbiomed.2021.104729
School/Department: School of Science, Technology and Health
URI: https://ray.yorksj.ac.uk/id/eprint/8171

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