ABSTRACT
In response to the growing threat posed by COVID-19, several initiatives have been launched to develop methods of halting the progression of the disease. In order to diagnose the COVID-19 infection, testing kits were utilized;however, the use of these kits is time-consuming and suffers from a lack of quality control measures. Computed tomography is an essential part of the diagnostic process in the treatment of COVID-19 (CT). The process of disease detection and diagnosis could be sped up with the help of automation, which would cut down on the number of exams that need to be carried out. A number of recently developed deep learning tools make it possible to automate the Covid-19 scanning process in CT scans and provide additional assistance. This paper investigates how to quickly identify COVID-19 using computational tomography (CT) scans, and it does so by using a deep learning technique that is derived from improving ResNet architecture. In order to test the proposed model, COVID-19 CT scans that include a patient-based split are utilized. The accuracy of the model’s core components is 98.1%, with specificity at 97% and sensitivity at 98.6%. © 2022, Institute of Advanced Engineering and Science. All rights reserved.
ABSTRACT
The coronavirus (COVID-19) as in the study of which had a starting point in China in 2019, has spread rapidly in every single country and has spread in millions of cases. The pandemic attracts lots of attentions due to major impacts not only on human health but on many other aspects including, social and political ones. This paper presents a robust data-driven machine learning analysis of COVID19 starting from data collection to the final step of knowledge extraction based on the selected research topics. The proposed approach evaluates the impact of social distancing on COVID19. Several machine learning and ensemble models have been used and compared to obtain the best accuracy. Experiments have been demonstrated on large public datasets. The motivation of this study is to propose an analytical machine learning based model to explore the social distancing aspects of COVID-19 pandemic. The proposed analytical model includes classic classifiers, distinctive ensemble methods such as bagging, feature based ensemble, voting and stacking. Also, it uses different Python libraries, Rattle, RStudio, Anaconda, and Jupyter Notebook. This study shows superior prediction performance comparing with the related approaches and the classical machine learning approaches. © 2022 IEEE.
ABSTRACT
Since the increasing risk of COVID-19, a set of actions have been achieved to develop tools to handle the spreading of the COVID-19 disease. Though testing kits were being used to diagnose the COVID-19 infection, the process requires time and the test kits suffer from being lack. In COVID-19 management,the computed tomography (CT) is considered an important diagnostic method. Taking into account largenumber of exams performed in high case-load situations, an automated method may help to encourage and save time for diagnosing and identifying the disease. Several deep learning tools have recently beendeveloped for COVID-19 scanning in CT scans as a technique for COVID-19 automation and diagnosticassistance. This article aims to explore the rapid recognition of COVID-19 and proposes an advanced deep learning technique, derived from improving the ResNet architecture as a transfer learning model. The architecture design of the proposed model is based on alleviating the connections between the blocks of the ResNet-50 model. This reduces the training time for scale-ability and handles the problem of vanishing gradient with relevant features for recognizing COVID-19 from CT images. The proposed model is evaluated using two well-known datasets of COVID-19 CT examined with a patient-based split. The proposed model attains a total back-bone accuracy of 98.1% with 97%, and 98.6% specificity and sensitivity, respectively. © 2022 Little Lion Scientific