Identifying COVID19 from Chest CT Images: A Deep Convolutional Neural Networks Based Approach

Mishra, Arnab Kumar; Das, Subhraseema; Roy, Pinki; Bandyopadhyay, Sivaji

doi:10.1155/2020/8843664

Cited by 124 publications

(68 citation statements)

References 12 publications

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“…2. These three methodologies are (1) Real-Time reverse transcriptase-Polymerase Chain Reaction (RT-PCR) (Tahamtan and Ardebili 2020;Waller et al 2020;Li et al 2020a, d), (2) chest CT imaging scan (Mishra et al 2020;Li et al 2020a, e;Kovács et al 2020), and (3) numerical laboratory tests (Brinati et al 2020;Kukar et al 2020;Cabitza et al 2020;Qiu et al 2020). RT-PCR tests are fairly quick, sensitive, and reliable.…”

Section: Covid-19 Diagnose Methodologiesmentioning

confidence: 99%

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

Mansour¹,

Saleh

Badawy

et al. 2021

J Ambient Intell Human Comput

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The outbreak of Coronavirus has spread between people around the world at a rapid rate so that the number of infected people and deaths is increasing quickly every day. Accordingly, it is a vital process to detect positive cases at an early stage for treatment and controlling the disease from spreading. Several medical tests had been applied for COVID-19 detection in certain injuries, but with limited efficiency. In this study, a new COVID-19 diagnosis strategy called Feature Correlated Naïve Bayes (FCNB) has been introduced. The FCNB consists of four phases, which are; Feature Selection Phase (FSP), Feature Clustering Phase (FCP), Master Feature Weighting Phase (MFWP), and Feature Correlated Naïve Bayes Phase (FCNBP). The FSP selects only the most effective features among the extracted features from laboratory tests for both COVID-19 patients and non-COVID-19 people by using the Genetic Algorithm as a wrapper method. The FCP constructs many clusters of features based on the selected features from FSP by using a novel clustering technique. These clusters of features are called Master Features (MFs) in which each MF contains a set of dependent features. The MFWP assigns a weight value to each MF by using a new weight calculation method. The FCNBP is used to classify patients based on the weighted Naïve Bayes algorithm with many modifications as the correlation between features. The proposed FCNB strategy has been compared to recent competitive techniques. Experimental results have proven the effectiveness of the FCNB strategy in which it outperforms recent competitive techniques because it achieves the maximum (99%) detection accuracy.

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Section: Covid-19 Diagnose Methodologiesmentioning

confidence: 99%

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

Mansour¹,

Saleh

Badawy

et al. 2021

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…Preprocessing and extracting ROIs using U-net increase the complexity of this algorithm. Multiple DL networks have been proposed [18] to extract graphical features of and diagnose COVID-19. An early screening method was proposed for COVID-19 using DL networks through CT images with an efficiency of 86.7% [19].…”

Section: Related Workmentioning

confidence: 99%

Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network

et al. 2021

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The quick spread of coronavirus disease has resulted in a global pandemic and more than fifteen million confirmed cases. To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis. Automatic identification software tools are essential for helping to screen COVID-19 using CT images. However, there are few datasets available, making it difficult to train deep learning (DL) networks. To address this issue, a generative adversarial network (GAN) is proposed in this work to generate more CT images. The Whale Optimization Algorithm (WOA) is used to optimize the hyperparameters of GAN's generator. The proposed method is tested and validated with different classification and meta-heuristics algorithms using the SARS-CoV-2 CT-Scan dataset, consisting of COVID-19 and non-COVID-19 images. The performance metrics of the proposed optimized model, including accuracy (99.22%), sensitivity (99.78%), specificity (97.78%), F1-score (98.79%), positive predictive value (97.82%), and negative predictive value (99.77%), as well as its confusion matrix and receiver operating characteristic (ROC) curves, indicate that it performs better than state-of-the-art methods. This proposed model will help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks.

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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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Identifying COVID19 from Chest CT Images: A Deep Convolutional Neural Networks Based Approach

Cited by 124 publications

References 12 publications

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network

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

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