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. 2021 Dec 27:2021:2560388.
doi: 10.1155/2021/2560388. eCollection 2021.

A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images

Hassaan Haider Syed  1 , Muhammad Attique Khan  1 , Usman Tariq  2 , Ammar Armghan  3 , Fayadh Alenezi  3 , Junaid Ali Khan  1 , Seungmin Rho  4 , Seifedine Kadry  5 , Venkatesan Rajinikanth  6
Affiliations

A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images

Hassaan Haider Syed et al. Behav Neurol. .

Retraction in

Abstract

The excessive number of COVID-19 cases reported worldwide so far, supplemented by a high rate of false alarms in its diagnosis using the conventional polymerase chain reaction method, has led to an increased number of high-resolution computed tomography (CT) examinations conducted. The manual inspection of the latter, besides being slow, is susceptible to human errors, especially because of an uncanny resemblance between the CT scans of COVID-19 and those of pneumonia, and therefore demands a proportional increase in the number of expert radiologists. Artificial intelligence-based computer-aided diagnosis of COVID-19 using the CT scans has been recently coined, which has proven its effectiveness in terms of accuracy and computation time. In this work, a similar framework for classification of COVID-19 using CT scans is proposed. The proposed method includes four core steps: (i) preparing a database of three different classes such as COVID-19, pneumonia, and normal; (ii) modifying three pretrained deep learning models such as VGG16, ResNet50, and ResNet101 for the classification of COVID-19-positive scans; (iii) proposing an activation function and improving the firefly algorithm for feature selection; and (iv) fusing optimal selected features using descending order serial approach and classifying using multiclass supervised learning algorithms. We demonstrate that once this method is performed on a publicly available dataset, this system attains an improved accuracy of 97.9% and the computational time is almost 34 (sec).

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Conflict of interest statement

All authors declare that they have no conflict of interest in this work.

Figures

Figure 1
Figure 1
The proposed multiclass architecture of COVID-19 classification using deep learning feature selection and fusion.
Figure 2
Figure 2
Sample CT images considered from the prepared dataset.
Figure 3
Figure 3
Original architecture of VGG16 CNN model.
Figure 4
Figure 4
Architecture of modified VGG16 for COVID-19 classification using CT images.
Figure 5
Figure 5
Architecture of ResNet50 for image classification.
Figure 6
Figure 6
Architecture of modified ResNet50 for the classification of COVID-19 CT images.
Figure 7
Figure 7
Architecture of Resnet101 for image classification.
Figure 8
Figure 8
Architecture of modified ResNet101 for the classification of COVID-19 CT scans.
Figure 9
Figure 9
Transfer learning architecture.
Figure 10
Figure 10
Confusion matrix of Cubic SVM for experiment 1.
Figure 11
Figure 11
Confusion matrix of Cubic SVM for experiment 2.
Figure 12
Figure 12
Confusion matrix of Cubic SVM for experiment 3.
Figure 13
Figure 13
Confusion matrix of Cubic SVM for experiment 4.
See this image and copyright information in PMC

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