CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images

Abstract

This paper focuses on the application of deep learning (DL) based model in the analysis of novel coronavirus disease (COVID-19) from X-ray images. The novelty of this work is in the development of a new DL algorithm termed as optimized residual network (CO-ResNet) for COVID-19. The proposed CO-ResNet is developed by applying hyperparameter tuning to the conventional ResNet 101. CO-ResNet is applied to a novel dataset of 5,935 X-ray images retrieved from two publicly available datasets. By utilizing resizing, augmentation and normalization and testing different epochs our CO-ResNet was optimized for detecting COVID-19 versus pneumonia with normal healthy lung controls. Different evaluation metrics such as the classification accuracy, F1 score, recall, precision, area under the receiver operating characteristics curve (AUC) are used. Our proposed CO-ResNet obtains consistently best performance in the multi-level data classification problem, including health lung, pneumonia affected lung and COVID-19 affected lung samples. In the experimental evaluation, the detection rate accuracy in discerning COVID-19 is 98.74%, and for healthy normal lungs, pneumonia affected lungs are 92.08% and 91.32% respectively for our CO-ResNet with ResNet101 backbone. Further, our model obtained accuracy values of 83.68% and 82% for healthy normal lungs and pneumonia affected lungs with ResNet152 backbone. Experimental results indicate the potential usage of our new DL driven model for classification of COVID-19 and pneumonia.

A review on epidemiology, genomic characteristics, spread, and treatments of COVID-19

“…Therefore some machine learning methods [ [85] , [86] , [87] , [88] , [89] , [90] , [91] ] can be helpful for predicting COVID-19. Moreover, deep learning methods [ [92] , [93] , [94] ] can be potentially used to detect COVID-19 at the initial stage by applying CT and X-ray images [ [96] , [97] , [98] , [99] ].…”

Federated learning: Applications, challenges and future directions

“…Classification algorithms in machine learning [70] learn how to categorize or annotate a given collection of occurrences with labels or classes. For example, the classification tasks of COVID-19 detections [71][72][73][74][75][76][77][78][79][80][81][82][83][84], cancer diagnoses [85][86][87][88][89][90][91][92][93][94] and autism spectrum disorder (ASD) [84,[95][96][97] are considered in a FL setting in healthcare. Other classification tasks studied include emotion recognition and human activity [98][99][100] and prediction of patient hospitalization [101,102].…”

E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network

“…Empirical analysis have revealed 98.74% as detection accuracy for normal lungs and 92.08% for pneumonia. Though better prediction rate has been attained, accuracy has to be enhanced further ( Bharati et al, 2021a ). Additionally, Deep CNN based method has been recommended to detect patients having COVID-19 through the use of CX-R images.…”