Rapid Identification of COVID-19 Severity in CT Scans through Classification of Deep Features

Yu, Zekuan; Li, Xiaohu; Sun, Haitao; Wang, Jian; Zhao, Tongtong; Chen, Hongyi; Ma, Yun; Zhu, Shujin; Xie, Zongyu

doi:10.21203/rs.3.rs-30802/v1

Cited by 14 publications

(30 citation statements)

References 15 publications

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“…Although the diagnosis of COVID-19 relies on the detection of the SARS-CoV-2 RNA by Real-Time Reverse Transcriptase (RT)–PCR ( 4 , 12 , 13 ), here only a few endodontists were using it as their patient screening technique. No endodontist participant requested chest X-ray for COVID-19 screening in their dental practice, although chest X-ray might show patchy shadows and ground-glass opacity in the lung ( 14 ). It is worth pointing out that only a few participants reported uncooperative patients for the COVID-19 screening measurement adopted in their practice.…”

Section: Discussionmentioning

confidence: 99%

A Cross-sectional Survey on the Impact of Coronavirus Disease 2019 on the Clinical Practice of Endodontists across the United States

Martinho

Griffin

2021

Journal of Endodontics

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Introduction This survey investigated the effect of the COVID-19 pandemic on the clinical practice of endodontics among the AAE members by evaluating the impact on clinical activities, patient screening, infection control measurements, potential transmission, clinical protocols as well as psychological concerns. Material and Methods A descriptive, cross-sectional survey was developed to query AAE members from all seven districts. The survey consisted of 24 questions, eight demographic questions, and 16 questions related to the COVID-19 pandemic impact on the clinical practice. Results A total of 454 AAE members participated in the survey. As of July 2020, most endodontists were active in front line treatment of dental patients (82%). N95 respirator face mask was described by 83.1% of the participants as special measures beyond the regular PPE. Rubber dam isolation was recognized by the majority of the participants at some level to reduce the chance of COVID-19 cross-infection. Most of the endodontist participants acknowledged trauma followed by swelling, pain, postoperative complication to be emergencies. The majority of respondents reported being concerned about the effect of COVID-19 upon their practice. No differences in worries about COVID-19 infection were related to demographics (p>.05). Conclusions The majority of the Endodontists are aware of the COVID-19 pandemic, taking special precautions, and are concerned about contracting and spreading the virus. Despite the conflict between their roles as health care providers and family members with the potential risk of exposing their families, most of them remain on duty providing front line care for dental treatment.

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Section: Discussionmentioning

confidence: 99%

A Cross-sectional Survey on the Impact of Coronavirus Disease 2019 on the Clinical Practice of Endodontists across the United States

Martinho

Griffin

2021

Journal of Endodontics

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“…For example, Yue et al. [38] used 729 CT scan images of Covid-19 patients to measure the severity. They used a pre-trained deep neural network to classify the severity and achieved an overall detection accuracy of 95.34%.…”

Section: Related Workmentioning

confidence: 99%

“…It is due to the Covid-19 image’s large input size. Fine-tuning or transfer learning is used to tackle this problem in published researches [34] , [35] , [36] , [37] , [38] , [40] . The ImageNet dataset was used in the pre-training of most of these methods.…”

Section: Related Workmentioning

confidence: 99%

A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images

Naeem

Bin-Salem

2021

Applied Soft Computing

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Auto-detection of diseases has become a prime issue in medical sciences as population density is fast growing. An intelligent framework for disease detection helps physicians identify illnesses, give reliable and consistent results, and reduce death rates. Coronavirus (Covid-19) has recently been one of the most severe and acute diseases in the world. An automatic detection framework should therefore be introduced as the fastest diagnostic alternative to avoid Covid-19 spread. In this paper, an automatic Covid-19 identification in the CT scan and chest X-ray is obtained with the help of a combined deep learning and multi-level feature extraction methodology. In this method, the multi-level feature extraction approach comprises GIST, Scale Invariant Feature Transform (SIFT), and Convolutional Neural Network (CNN) extract features from CT scans and chest X-rays. The objective of multi-level feature extraction is to reduce the training complexity of CNN network, which significantly assists in accurate and robust Covid-19 identification. Finally, Long Short-Term Memory (LSTM) along the CNN network is used to detect the extracted Covid-19 features. The Kaggle SARS-CoV-2 CT scan dataset and the Italian SIRM Covid-19 CT scan and chest X-ray dataset were employed for testing purposes. Experimental outcomes show that proposed approach obtained 98.94% accuracy with the SARS-CoV-2 CT scan dataset and 83.03% accuracy with the SIRM Covid-19 CT scan and chest X-ray dataset. The proposed approach helps radiologists and practitioners to detect and treat Covid-19 cases effectively over the pandemic.

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“…Yu 24 et al explored four pre-trained DL models (Inception-V3, ResNet-50, ResNet-101 and DenseNet-201) to extract the high-level resources of CT. These resources were then provided to various classifiers (linear discriminant, linear SVM, cubic SVM, k-NN and Adaboost with decision trees) to identify serious and non-serious cases of COVID-19.…”

Section: Related Workmentioning

confidence: 99%

System for Quantitative Diagnosis of COVID-19-Associated Pneumonia Based on Superpixels With Deep Learning and Chest CT

Tetila¹,

Bressem²,

Astolfi³

et al. 2020

Preprint

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COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can lead to complications such as acute respiratory distress syndrome, acute heart injury and secondary infections in a relatively high proportion of patients and, consequently, significant mortality. The definitive diagnosis of COVID-19 is performed by real-time Polymerase Chain Reaction (RT-PCR). However, as the result of RT-PCR, at least for now, has been made available within a longer period of time than the computed tomography (CT) report, this has taken on an important role in the detection of patients infected with COVID-19. A rough estimate of the extent of lung involvement by the disease is also important and considered an additional criterion for deciding on discharge or hospitalization. Recent research has adopted deep neural networks and other machine learning approaches to detect the presence of lung infection caused by COVID-19. However, the extent of lung involvement (volume) caused by the disease has been little investigated. In this work, we created an end-to-end computer vision system to automatically quantify the Percentage Of Infection (POI) in chest CT images of COVID-19 cases confirmed by the laboratory. Trying to obtain high accuracy, we evaluated the performance of three deep neural networks well known in the literature, trained with three different training strategies: (1) no transfer learning with randomly initialized weights; (2) transfer learning without fine-tuning with ImageNet weights; and (3) transfer learning with 100% fine-tuning. Data augmentation and dropout were used during networks training to reduce overfitting and increase the generalization capacity of the models. Our approach consists of segmenting a chest CT with the SLIC Superpixels method and classifying each segment (superpixel) into a specific class (COVID or non-COVID). We used the weights of the deep neural network best evaluated for accuracy in our computer vision system in order to classify the superpixels in the image and quantify the regions of COVID-19 infection, thus calculating the POI on chest CT. The results indicate that deep learning models can be successfully used to support radiologists in the quantitative diagnosis of lung infection caused by COVID-19, reaching an accuracy of up to 98.4% with Inception-Resnet-v2 architecture.

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Rapid Identification of COVID-19 Severity in CT Scans through Classification of Deep Features

Cited by 14 publications

References 15 publications

A Cross-sectional Survey on the Impact of Coronavirus Disease 2019 on the Clinical Practice of Endodontists across the United States

A Cross-sectional Survey on the Impact of Coronavirus Disease 2019 on the Clinical Practice of Endodontists across the United States

A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images

System for Quantitative Diagnosis of COVID-19-Associated Pneumonia Based on Superpixels With Deep Learning and Chest CT

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