Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Chandra, Tej Bahadur; Verma, Kesari; Singh, Bikesh Kumar; Jain, Deepak; Netam, Satyabhuwan Singh

doi:10.1016/j.eswa.2020.113909

Cited by 240 publications

(204 citation statements)

References 47 publications

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“…Moreover, the significantly better performance of SVM in comparison to Random Forest (RF) and Extreme Boosting Machine (XGB) is attributed to its ability to deal well with high dimensional features with fewer examples as in our case. Moreover, features extracted through pre-trained deep learning models perform better than handcrafted ones as in the case of the study performed by Chandra et al, [ 15 ].…”

Section: Resultsmentioning

confidence: 99%

“…In the meanwhile, CT scans of COVID-19 infected patients show diverse features and manual interpretation of these scans with subtle variations is quite challenging [ 13 ]. Moreover, the current enormous upsurge of infected patients makes it a challenging task for the domain experts to complete a timely diagnosis [ 14 , 15 ]. Therefore, some Computer-Aided Diagnostic (CAD) systems are required to better manipulate and understand the CCT images.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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“…It can act as an alternative screening modality for the detection of COVID-19 or to validate the related diagnosis. [15] …”

Section: Discussionmentioning

confidence: 99%

Management of severe COVID-19 patient with negative RT-PCR for SARS-CoV-2: Role of clinical, radiological, and serological diagnosis

Soedarsono¹,

Febriani²,

Hasan³

et al. 2021

Radiology Case Reports

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A real-time reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard in diagnosis for infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the false-negative result is the problem in the prevention and control the pandemic of coronavirus disease 2019 (COVID-19). A false-negative of RT-PCR test needs to be evaluated when the patient showed a high clinical suspicion for COVID-19. We report a 36-year-old man with four times negative RT-PCR results, but clinical, radiological (chest X-ray and chest CT scan), and serological examinations showed a high suspicion of COVID-19. History of close contacted with COVID-19 confirmed patient was reported, and the wife of our case was also RT-PCR tested positive for SARS-CoV-2 in the next few days strengthen the COVID-19 diagnosis of our case patient. It is important to use the combination of RT-PCR, chest X-ray, chest CT scan, clinical manifestations, antibodies test, and exposure history of patients to diagnose COVID-19 and decide the early isolation and appropriate treatment.

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“…There are some other techniques other than deep learning which were used for the automatic detection of COVID-19 like in 32 where an ensemble classifier was used as an automatic screening system for diagnosing covid-19 from the chest x-ray where the accuracy was around 98% but was tested on small size dataset. Also, a kernel and linear kernel SVM classification model was developed in 33 to carry out 2 classifications (covid and normal) and 3 classifications (covid, pneumonia and normal) after a feature extraction procedure using several forms of convolutionary neural network.…”

Section: Algorithm 1: Features Extraction Algorithmmentioning

confidence: 99%

ANFIS-Net for Automatic Detection of COVID-19

Al-Ali

Elharrouss

Qidwai

et al. 2021

Preprint

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Among the most leading causes of mortality across the globe is infectious diseases. Human Immunodeficiency Virus infection and Acquired Immune Deficiency Syndrome (HIV/AIDS), H1N1 influenza and Poliomyelitis, Severe Acute Respiratory Syndrome (SARS) are all infectious diseases that has cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control, has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As Fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic detection of chest X-ray Images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. The results were promising, reaching 98.67% accuracy compared with the other state-of-the-art techniques.

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Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Cited by 240 publications

References 47 publications

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

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

Management of severe COVID-19 patient with negative RT-PCR for SARS-CoV-2: Role of clinical, radiological, and serological diagnosis

ANFIS-Net for Automatic Detection of COVID-19

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