Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans

Abstract

Several studies suggest that COVID-19 may be accompanied by symptoms such as a dry cough, muscle aches, sore throat, and mild to moderate respiratory illness. The symptoms of this disease indicate the fact that COVID-19 causes noticeable negative effects on the lungs. Therefore, considering the health status of the lungs using X-rays and CT scans of the chest can significantly help diagnose COVID-19 infection. Due to the fact that most of the methods that have been proposed to COVID-19 diagnose deal with the lengthy testing time and also might give more false positive and false negative results, this paper aims to review and implement artificial intelligence (AI) image-based diagnosis methods in order to detect coronavirus infection with zero or near to zero false positives and false negatives rates. Besides the already existing AI image-based medical diagnosis method for the other well-known disease, this study aims on finding the most accurate COVID-19 detection method among AI methods such as machine learning (ML) and artificial neural network (ANN), ensemble learning (EL) methods.

Densely Connected Convolutional Networks (DenseNet) for Diagnosing Coronavirus Disease (COVID-19) from Chest X-ray Imaging

“…The pandemic event called COVID19 has significantly affected the performance of centers such as small clinics, doctors' offices, emergency care centers and large hospitals with emergency rooms. [6,7]. The use of intelligent systems plays a considerable role in the process of treating the patient.…”

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

“…X-rays have a precision of 98 percent and a recall of 100 percent, whereas CT scans have a precision of 97 percent and a recall of 97 percent. Tabrizchi et al proposed a paper [96] in 2020 for using various machine learning, ensemble learning, and neural networks, such as SVM, Nave Bayes, CNN, MLP, Adaboost, and GDBT, to find an intelligent and accurate solution for COVID-19 detection, and they discovered that SVM and CNN outperformed all of them, with accuracy of 0.9920 and 0.9670, and precision of 0.9819 and 0.9724. Sharma submitted research [97] in 2020 that looked at CT images of 200 infected individuals and utilized machine learning to detect COVID-19.…”

Untitled

“…Unsupervised learning employs unlabeled or unknown data, requiring more analysis than supervised learning. Without human assistance, it autonomously assesses options and spots patterns 28 . Many COVID-19 detection methods exist in addition to conventional identification techniques.…”