“An Enhanced Novel Corona virus (COVID-19) Detection Model Based on Lung X-Ray Image using Convolutional Neural Netwo
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Date
2022-11
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Department of Electronic and Telecommunication Engineering
Abstract
“The first cases of COVID-19 were discovered in December of 2019. Extremely contagious with no cure in sight, the only option was to seek out and quarantine anyone who had contracted it. As a result, scientists are beginning to use chest X-rays (CXRs), a simple and cheap diagnostic tool. Because of a shortage in diagnostic tools, scientists developed an automated systems to combine chest X-rays for a more accurate diagnosis. The purpose of this study is to employ artificial intelligence to train CNN models using raw chest X-rays and CT scans. Binary or multi-class classifications and transfer learning are used. Various datasets' characteristics are offered, and they are used for training and verifying models. Algorithm performance is measured in terms of accuracy, precision, recall, and F1 score. As compared to conventional methods like polymerase chain reaction (PCR) testing, deep learning provides more accurate predictions of patient severity. Automatic CXR detection tools help radiologists screen for and make an accurate diagnosis of infectious disease in patients. They're widely used because of their low cost, accessibility, and speedy outcomes. With a 99.2% detection rate, 97.5 % accuracy, and 98 % F1-score, the proposed method shows potential for detecting people infected with COVID-19.”
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Najmuj Jakib, T181019