Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Ardakani, Ali Abbasian; Kanafi, Alireza Rajabzadeh; Acharya, U. Rajendra; Khadem, Nazanin; Mohammadi, Afshin

doi:10.1016/j.compbiomed.2020.103795

Cited by 753 publications

(616 citation statements)

References 35 publications

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“…The authors in Bai et al (2020) showed that their DL based CAD system achieved higher test accuracy (96% vs. 85%), sensitivity (95% vs. 79%), and specificity (96% vs. 88%) than radiologists. Whereas in Ardakani et al (2020) the performance of the radiologist was lower than the authors' proposed DL-based CAD system, with a sensitivity of (89.21% vs. 98.04%), specificity of (83.33% vs. 98.04%), and accuracy of (86.27% vs. 99.5%). On the other hand, in Chen et al (2020), the authors showed that their DL based CAD system can reduce the manual radiologist's diagnosis time by 65%.…”

Section: Discussioncontrasting

confidence: 55%

“…The outperformance of CNN was proven in the related studies by various authors (Bai et al, 2020;Chen et al, 2020;Ardakani et al, 2020), who compared the performance of their CAD system based on DL techniques, with that of a trained radiologist without the help of a CAD system. These studies indicated that the performance of DL based CAD systems outperformed manual diagnosis by a trained radiologist without the help of a CAD system.…”

Section: Discussionmentioning

confidence: 95%

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MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks

Attallah¹,

Ragab²,

Sharkas³

2020

PeerJ

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Coronavirus (COVID-19) was first observed in Wuhan, China, and quickly propagated worldwide. It is considered the supreme crisis of the present era and one of the most crucial hazards threatening worldwide health. Therefore, the early detection of COVID-19 is essential. The common way to detect COVID-19 is the reverse transcription-polymerase chain reaction (RT-PCR) test, although it has several drawbacks. Computed tomography (CT) scans can enable the early detection of suspected patients, however, the overlap between patterns of COVID-19 and other types of pneumonia makes it difficult for radiologists to diagnose COVID-19 accurately. On the other hand, deep learning (DL) techniques and especially the convolutional neural network (CNN) can classify COVID-19 and non-COVID-19 cases. In addition, DL techniques that use CT images can deliver an accurate diagnosis faster than the RT-PCR test, which consequently saves time for disease control and provides an efficient computer-aided diagnosis (CAD) system. The shortage of publicly available datasets of CT images, makes the CAD system’s design a challenging task. The CAD systems in the literature are based on either individual CNN or two-fused CNNs; one used for segmentation and the other for classification and diagnosis. In this article, a novel CAD system is proposed for diagnosing COVID-19 based on the fusion of multiple CNNs. First, an end-to-end classification is performed. Afterward, the deep features are extracted from each network individually and classified using a support vector machine (SVM) classifier. Next, principal component analysis is applied to each deep feature set, extracted from each network. Such feature sets are then used to train an SVM classifier individually. Afterward, a selected number of principal components from each deep feature set are fused and compared with the fusion of the deep features extracted from each CNN. The results show that the proposed system is effective and capable of detecting COVID-19 and distinguishing it from non-COVID-19 cases with an accuracy of 94.7%, AUC of 0.98 (98%), sensitivity 95.6%, and specificity of 93.7%. Moreover, the results show that the system is efficient, as fusing a selected number of principal components has reduced the computational cost of the final model by almost 32%.

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Section: Discussioncontrasting

confidence: 55%

Section: Discussionmentioning

confidence: 95%

MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks

Attallah¹,

Ragab²,

Sharkas³

2020

PeerJ

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“…These extracted features are then made the basis of the detection of COVID-19. This COVID Diagnosis System (InstaCovNet-19) is internally a deep learning model which consist of various pre-trained models such as ResNet-101 [6] , Inception v3 [7] , Xception [8] , MobileNetv2 [9] and NASNet [10] . These pre-trained models are then combined using the Integrated stacking technique, where the features are combined to produce the most accurate results.…”

Section: Introductionmentioning

confidence: 99%

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

Gupta

Anjum

Gupta

et al. 2021

Applied Soft Computing

187

121

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Recently, the whole world became infected by the newly discovered coronavirus (COVID-19). SARS-CoV-2, or widely known as COVID-19, has proved to be a hazardous virus severely affecting the health of people. It causes respiratory illness, especially in people who already suffer from other diseases. Limited availability of test kits as well as symptoms similar to other diseases such as pneumonia has made this disease deadly, claiming the lives of millions of people. Artificial intelligence models are found to be very successful in the diagnosis of various diseases in the biomedical field In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet to compensate for a relatively small amount of training data. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities caused by such diseases in Chest X-ray images of the person infected. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification while achieving an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification. The proposed model achieves an average recall, F1 score, and precision of 99%, 99%, and 99%, respectively on ternary classification, while achieving a 100% precision and a recall of 99% on the binary class., while achieving a 100% precision and a recall of 99% on the COVID class. InstaCovNet-19’s ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine.

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“…For COVID detection, AlexNet, VGGNet, GoogleNet, MobileNet, ResNet, SqueezeNet and Xception architectures were used. The best results were obtained with ResNet-101 and Xception [21]. In another study on CT images, a deep learning model was developed on 4352 images collected from 332 patients.…”

mentioning

confidence: 99%

“…MobileNet is based on linear bottlenecks and inverted residual. It starts convolution layers, followed by inverted residual blocks, linear bottlenecks blocks, convolution layer and fully connected dense layer[21]. Inception-V3 was improved version of the GoogleNet (In 2015).…”

mentioning

confidence: 99%

COVID-19 detection in Chest X-ray Images using Deep Learning

Erdem¹,

Aydın²

2020

Preprint

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COVID-19 is an important threat worldwide. This disease is caused by the novel SARS-CoV-2. CXR and CT images reveal specific information about the disease. However, when interpreting these images, experiencing an overlap with other lung infections complicates the detection of the disease. Due to this situation, the need for computer-aided systems is increasing day by day. In this study, solutions were developed with proposed models based on deep neural networks (DNN). All analyzes were performed on CXR data received on the publicly available. This paper offers a comparison of the deep learning models (SqueezeNet, Inception-V3, VGG16, MobileNet, Xception, VGG19+MobileNet (Concatenated)) that results in the detection and classification of disease. Empirical evaluations demonstrate that the Inception-V3 model gives 90% accuracy with 100% precision for the COVID-19 infection. This model has been provided with better results compared to other models.

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Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Cited by 753 publications

References 35 publications

MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks

MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

COVID-19 detection in Chest X-ray Images using Deep Learning

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