Computer-aided diagnosis of COVID-19 disease from chest X-ray images integrating deep feature extraction

The novel coronavirus (COVID-19) has an enormous impact on the daily lives and health of people residing in more than 200 nations. This article proposes a deep learning-based system for the rapid diagnosis of COVID-19. Chest x-ray radiograph images were used because recent findings revealed that these images contain salient features about COVID-19 disease. Transfer learning was performed using different pre-trained convolutional neural networks models for binary (normal and COVID-19) and triple (normal, COVID-19 and viral pneumonia) class problems. Deep features were extracted from a fully connected layer of the ResNET50v2 model and feature dimension was reduced through feature reduction methods. Feature fusion of feature sets reduced through analysis of variance (ANOVA) and mutual information feature selection (MIFS) was fed to Fine K-nearest neighbour to perform binary classification. Similarly, serial feature fusion of MIFS and chi-square features were utilized to train Medium Gaussian Support Vector Machines to distinguish normal, COVID-19 and viral pneumonia cases. The proposed framework yielded accuracies of 99.5% for binary and 95.5% for triple class experiments. The proposed model shows better performance than the existing methods, and this research has the potential to assist medical professionals to enhance the diagnostic ability to detect coronavirus disease. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Computer‐aided diagnosis of COVID‐19 disease from chest x‐ray images integrating deep feature extraction

The novel coronavirus (COVID‐19) has an enormous impact on the daily lives and health of people residing in more than 200 nations. This article proposes a deep learning‐based system for the rapid diagnosis of COVID‐19. Chest x‐ray radiograph images were used because recent findings revealed that these images contain salient features about COVID‐19 disease. Transfer learning was performed using different pre‐trained convolutional neural networks models for binary (normal and COVID‐19) and triple (normal, COVID‐19 and viral pneumonia) class problems. Deep features were extracted from a fully connected layer of the ResNET50v2 model and feature dimension was reduced through feature reduction methods. Feature fusion of feature sets reduced through analysis of variance (ANOVA) and mutual information feature selection (MIFS) was fed to Fine K‐nearest neighbour to perform binary classification. Similarly, serial feature fusion of MIFS and chi‐square features were utilized to train Medium Gaussian Support Vector Machines to distinguish normal, COVID‐19 and viral pneumonia cases. The proposed framework yielded accuracies of 99.5% for binary and 95.5% for triple class experiments. The proposed model shows better performance than the existing methods, and this research has the potential to assist medical professionals to enhance the diagnostic ability to detect coronavirus disease. [ FROM AUTHOR] Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Rapid Reorganization of an Academic Infectious Diseases Program During the Coronavirus Disease 2019 Pandemic in Detroit: A Novel Unit-based Group Rounding Model.

The surge of coronavirus disease 2019 (COVID-19) hospitalizations at our 877-bed quaternary care hospital in Detroit led to an emergent demand for Infectious Diseases (ID) consultations. The traditional 1-on-1 consultation model was untenable. Therefore, we rapidly restructured our ID division to provide effective consultative services. We implemented a novel unit-based group rounds model that focused on delivering key updates to teams and providing unit-wide consultations simultaneously to all team members. Effectiveness of the program was studied using Likert-scale survey data. The survey captured data from the first month of the Detroit COVID-19 pandemic. During this period there were approximately 950 patients hospitalized for treatment of COVID-19. The survey of trainees and faculty reported an overall 95% positive response to delivery of information, new knowledge acquisition, and provider confidence in the care of COVID-19 patients. This showed that the unit-based consult model is a sustainable effort to provide care during epidemics.