Ultra-high-resolution computed tomography can demonstrate alveolar collapse in novel coronavirus (COVID-19) pneumonia

Iwasawa, Tae; Sato, Midori; Yamaya, Takafumi; Sato, Yozo; Uchida, Yoshinori; Kitamura, Hideya; Hagiwara, Eri; Komatsu, Shigeru; Utsunomiya, Daisuke; Ogura, Takashi

doi:10.1007/s11604-020-00956-y

Cited by 63 publications

(61 citation statements)

References 15 publications

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“…1 ). In line with the previous study where different degrees of alveolar collapse were found in 6 COVID-19 patients ( Iwasawa et al, 2020 ), our findings indicate that the COVID-19 are generally accompanied by reduced volume of the lung and lung lobes, with increased density and mass. The pattern of CAP is similar with non-pneumonia subjects, except that the density of CAP patients is relatively larger.…”

supporting

confidence: 93%

Lung volume reduction and infection localization revealed in Big data CT imaging of COVID-19

Shi¹,

Wei²,

Xia³

et al. 2021

International Journal of Infectious Diseases

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supporting

confidence: 93%

Lung volume reduction and infection localization revealed in Big data CT imaging of COVID-19

Shi¹,

Wei²,

Xia³

et al. 2021

International Journal of Infectious Diseases

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“… Author Conclusion Fan L [13] Summarizes the CT imaging characteristics of COVID-19, clinical category, CT imaging performance of COVID-19 children, CT imaging difference between COVID-19 patients and other pulmonary inflammation patients. Iwasawa T [14] Ultra-high-resolution CT image can identify the terminal bronchiole in normal lungs. U-HR-CT can be used to detect abnormal lung volume reduction, which is essential for the early diagnosis and timely treatment of critical illness in COVID-19 patients.…”

Section: Introductionmentioning

confidence: 99%

The ensemble deep learning model for novel COVID-19 on CT images

Tao

Yang

et al. 2021

Applied Soft Computing

208

118

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The rapid detection of the novel coronavirus disease, COVID-19, has a positive effect on preventing propagation and enhancing therapeutic outcomes. This article focuses on the rapid detection of COVID-19. We propose an ensemble deep learning model for novel COVID-19 detection from CT images. 2933 lung CT images from COVID-19 patients were obtained from previous publications, authoritative media reports, and public databases. The images were preprocessed to obtain 2500 high-quality images. 2500 CT images of lung tumor and 2500 from normal lung were obtained from a hospital. Transfer learning was used to initialize model parameters and pretrain three deep convolutional neural network models: AlexNet, GoogleNet, and ResNet. These models were used for feature extraction on all images. Softmax was used as the classification algorithm of the fully connected layer. The ensemble classifier EDL-COVID was obtained via relative majority voting. Finally, the ensemble classifier was compared with three component classifiers to evaluate accuracy, sensitivity, specificity, F value, and Matthews correlation coefficient. The results showed that the overall classification performance of the ensemble model was better than that of the component classifier. The evaluation indexes were also higher. This algorithm can better meet the rapid detection requirements of the novel coronavirus disease COVID-19.

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“…Consequently, DL based-CAD systems that use medical images are recommended as another tool in the diagnosis and control of the novel coronavirus. Such CAD systems offer a quick and easy solution for medical diagnosis of COVID-19 (Iwasawa et al, 2020). Moreover, these CAD systems have the potential to deliver an accurate and fast second opinion, to help radiologists in giving an accurate diagnosis based on medical images (Ardakani et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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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Ultra-high-resolution computed tomography can demonstrate alveolar collapse in novel coronavirus (COVID-19) pneumonia

Cited by 63 publications

References 15 publications

Lung volume reduction and infection localization revealed in Big data CT imaging of COVID-19

Lung volume reduction and infection localization revealed in Big data CT imaging of COVID-19

The ensemble deep learning model for novel COVID-19 on CT images

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

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