Diagnosis of COVID-19 using CT scan images and deep learning techniques

Shah, Vruddhi; Keniya, Rinkal; Shridharani, Akanksha; Punjabi, Manav; Shah, Jainam; Mehendale, Ninad

doi:10.1101/2020.07.11.20151332

Cited by 16 publications

(12 citation statements)

References 11 publications

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“…Entirely novel deep neural network architectures have been explored in some studies for COVID-19 case detection. Shah et al ( 35 ) proposed a 10-layer 2D CNN called CTnet-10, but found that it was outperformed by pre-existing architectures. Zheng et al ( 37 ) proposed a 3D CNN called DeCovNet which operates on full 3D CT volumes.…”

Section: Discussionmentioning

confidence: 99%

COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

2020

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The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients have worsening respiratory status or developing complications that require expedited care, or patients are suspected to be COVID-19-positive but have negative RT-PCR test results. Early studies on CT-based screening have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, in this study we introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach. Additionally, we introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation comprising 104,009 images across 1,489 patient cases. Furthermore, in the interest of reliability and transparency, we leverage an explainability-driven performance validation strategy to investigate the decision-making behavior of COVIDNet-CT, and in doing so ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images. Both COVIDNet-CT and the COVIDx-CT dataset are available to the general public in an open-source and open access manner as part of the COVID-Net initiative. While COVIDNet-CT is not yet a production-ready screening solution, we hope that releasing the model and dataset will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.

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Section: Discussionmentioning

confidence: 99%

COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

2020

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“…Eighteen papers applied deep learning techniques to CT imaging, all of which were framed as a classification task to distinguish COVID-19 from other lung pathologies such as (viral or bacterial) pneumonia, interstitial lung disease 35,[40][41][42][43][44][45][46][47] and/or a non-COVID-19 class 40,41,44,46,[48][49][50][51][52] . The full three-dimensional (3D) volumes were only considered in seven papers 40,43,47,50,[52][53][54] with the remainder considering isolated 2D slices or even 2D patches 45 . In most 2D models, authors employed transfer learning, with networks pre-trained on ImageNet 55 .…”

Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

et al. 2021

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Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.

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“…We have searched existing techniques in the online literature using different keywords such as “Machine learning and COVID-19”, “COVID-19 diagnosis with CT scans”, and “COVID-19 diagnosis with CT scans and machine learning”. While going through the online existing literature (peer-reviewed) published in reputed journals, we found a plethora of machine learning techniques to diagnose COVID-19 using chest CT scans with varying sources and amount of training data [ [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] ]. All these previously published techniques can be categorized into three main classes as follows: Deep learning-based, transfer learning with fine-tuning a customized fully connected layer, shallow learning with handcrafted textured features.…”

Section: Related Workmentioning

confidence: 99%

“…Previously published studies employing deep learning-based machine learning techniques to detect COVID-19 mostly used Convolution Neural Network (CNN) [ 20 ] based architectures in their proposed design [ [23] , [24] , [25] , 29 , [31] , [32] , [33] , [34] , [35] , [36] , [37] ]. However, deep learning approaches to generalize well normally require an enormous amount of data which is not readily available right now [ 22 ].…”

Section: Related Workmentioning

confidence: 99%

COVIDC: An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology

Abbasi

Abbas

Andleeb

et al. 2021

Informatics in Medicine Unlocked

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Early diagnosis of Coronavirus disease 2019 (COVID-19) is significantly important, especially in the absence or inadequate provision of a specific vaccine, to stop the surge of this lethal infection by advising quarantine. This diagnosis is challenging as most of the patients having COVID-19 infection stay asymptomatic while others showing symptoms are hard to distinguish from patients having different respiratory infections such as severe flu and Pneumonia. Due to cost and time-consuming wet-lab diagnostic tests for COVID-19, there is an utmost requirement for some alternate, non-invasive, rapid, and discounted automatic screening system. A chest CT scan can effectively be used as an alternative modality to detect and diagnose the COVID-19 infection. In this study, we present an automatic COVID-19 diagnostic and severity prediction system called COVIDC (COVID-19 detection using CT scans) that uses deep feature maps from the chest CT scans for this purpose. Our newly proposed system not only detects COVID-19 but also predicts its severity by using a two-phase classification approach (COVID vs non-COVID, and COVID-19 severity) with deep feature maps and different shallow supervised classification algorithms such as SVMs and random forest to handle data scarcity. We performed a stringent COVIDC performance evaluation not only through 10-fold cross-validation and an external validation dataset but also in a real setting under the supervision of an experienced radiologist. In all the evaluation settings, COVIDC outperformed all the existing state-of-the-art methods designed to detect COVID-19 with an F1 score of 0.94 on the validation dataset and justified its use to diagnose COVID-19 effectively in the real setting by classifying correctly 9 out of 10 COVID-19 CT scans. We made COVIDC openly accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/covidc.

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Diagnosis of COVID-19 using CT scan images and deep learning techniques

Cited by 16 publications

References 11 publications

COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

COVIDC: An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology

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