Classification of COVID-19 Patients with their Severity Level from Chest CT Scans using Transfer Learning

• URL: http://arxiv.org/abs/2205.13774v1
• Date: Fri, 27 May 2022 06:22:09 GMT
• Title: Classification of COVID-19 Patients with their Severity Level from Chest CT Scans using Transfer Learning
• Authors: Mansi Gupta, Aman Swaraj, Karan Verma
• Abstract summary: The rapid increment in cases of COVID-19 has led to an increase in demand for hospital beds and other medical equipment. Keeping this in mind, we share our research in detecting COVID-19 as well as assessing its severity using chest-CT scans and Deep Learning pre-trained models. Our model can therefore help radiologists detect COVID-19 and the extent of its severity.
• Score: 3.667495151642095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background and Objective: During pandemics, the use of artificial intelligence (AI) approaches combined with biomedical science play a significant role in reducing the burden on the healthcare systems and physicians. The rapid increment in cases of COVID-19 has led to an increase in demand for hospital beds and other medical equipment. However, since medical facilities are limited, it is recommended to diagnose patients as per the severity of the infection. Keeping this in mind, we share our research in detecting COVID-19 as well as assessing its severity using chest-CT scans and Deep Learning pre-trained models. Dataset: We have collected a total of 1966 CT Scan images for three different class labels, namely, Non-COVID, Severe COVID, and Non-Severe COVID, out of which 714 CT images belong to the Non-COVID category, 713 CT images are for Non-Severe COVID category and 539 CT images are of Severe COVID category. Methods: All of the images are initially pre-processed using the Contrast Limited Histogram Equalization (CLAHE) approach. The pre-processed images are then fed into the VGG-16 network for extracting features. Finally, the retrieved characteristics are categorized and the accuracy is evaluated using a support vector machine (SVM) with 10-fold cross-validation (CV). Result and Conclusion: In our study, we have combined well-known strategies for pre-processing, feature extraction, and classification which brings us to a remarkable success rate of disease and its severity recognition with an accuracy of 96.05% (97.7% for Non-Severe COVID-19 images and 93% for Severe COVID-19 images). Our model can therefore help radiologists detect COVID-19 and the extent of its severity.

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