Shallow Convolutional Neural Network for COVID-19 Outbreak Screening using Chest X-rays

Mukherjee, Himadri; Ghosh, Santosh; Dhar, Ankita; Obaidullah, Sk Md; Santosh, K. C.; Roy, Kaushik

doi:10.36227/techrxiv.12156522.v1

Cited by 25 publications

(16 citation statements)

References 4 publications

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“…From the clinical application perspective, predictive tools based on CT imaging as well as on chest X-rays to enable accurate diagnosis of COVID-19 have flourished. Mukherjee et al (2020) established a convolutional neural network combined with chest X-rays for COVID-19 outbreak screening. Rajinikanth et al (2020) proposed a Harmony Search and Otsu-based system to detect COVID-19 using CT scan images.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019

Xiao

Sun

et al. 2020

Front. Bioeng. Biotechnol.

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Objectives: Coronavirus disease 2019 (COVID-19) is sweeping the globe and has resulted in infections in millions of people. Patients with COVID-19 face a high fatality risk once symptoms worsen; therefore, early identification of severely ill patients can enable early intervention, prevent disease progression, and help reduce mortality. This study aims to develop an artificial intelligence-assisted tool using computed tomography (CT) imaging to predict disease severity and further estimate the risk of developing severe disease in patients suffering from COVID-19. Materials and Methods: Initial CT images of 408 confirmed COVID-19 patients were retrospectively collected between January 1, 2020 and March 18, 2020 from hospitals in Honghu and Nanchang. The data of 303 patients in the People's Hospital of Honghu were assigned as the training data, and those of 105 patients in The First Affiliated Hospital of Nanchang University were assigned as the test dataset. A deep learning based-model using multiple instance learning and residual convolutional neural network (ResNet34) was developed and validated. The discrimination ability and prediction accuracy of the model were evaluated using the receiver operating characteristic curve and confusion matrix, respectively. Results: The deep learning-based model had an area under the curve (AUC) of 0.987 (95% confidence interval [CI]: 0.968-1.00) and an accuracy of 97.4% in the training set, whereas it had an AUC of 0.892 (0.828-0.955) and an accuracy of 81.9% in the test set. In the subgroup analysis of patients who had non-severe COVID-19 on admission, the

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019

Xiao

Sun

et al. 2020

Front. Bioeng. Biotechnol.

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“…•Mentioned an accuracy of 0.996 (95%CI: 0.9891.00) for COVID-19 vs Non-COVID-19 cases in CT studies, and calculated a sensitivity: 98.2% and specificity: 92.2%. H. Mukherjee et.al [93] A shallow CNN-based automatic COVID-19 cases detected using chest X-rays.…”

Section: Deep Learning Approaches To Mitigate Pandemic 411 Potentialitymentioning

confidence: 99%

A Survey on Deep Transfer Learning and Edge Computing for Mitigating the COVID-19 Pandemic

Sufian¹,

Ghosh²,

Sadiq³

et al. 2020

Preprint

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Highlights of the article are:<div>• Presented a systematic study of Deep Learning (DL), Deep Transfer Learning (DTL) and Edge Computing(EC) to mitigate COVID-19.</div><div>• Surveyed on existing DL, DTL, EC, and Dataset to mitigate pandemics with potentialities and challenges. </div><div>• Drawn a precedent pipeline model of DTL over EC for a future scope to mitigate any outbreaks.</div><div>• Given brief analyses and challenges wherever relevant in perspective of COVID-19.</div>

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“…Various ML models are discussed in the literature however for better accuracy deep leaning models can be used for better predictions [20,21,22]. Furthermore, predictions can be more accurate using active learning models in this multitudinal and multimodal data used for predictions instead of single type of data [23].…”

Section: Related Workmentioning

confidence: 99%

Data Analytics: COVID-19 Prediction Using Multimodal Data

Mahalle

Sable

Mahalle

et al. 2020

Preprint

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Globally, there is massive uptake and explosion of data and challenge is to address issues like scale, pace, velocity, variety, volume and complexity of this big data. Considering the recent epidemic in China, modeling of COVID-19 epidemic for cumulative number of infected cases using data available in early phase was big challenge. Being COVID-19 pandemic during very short time span, it is very important to analyze the trend of these spread and infected cases. This chapter presents medical perspective of COVID-19 towards epidemiological triad and the study of state-of-the-art. The main aim this chapter is to present different predictive analytics techniques available for trend analysis, different models and algorithms and their comparison. Finally, this chapter concludes with the prediction of COVID-19 using Prophet algorithm indicating more faster spread in short term. These predictions will be useful to government and healthcare communities to initiate appropriate measures to control this outbreak in time.

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Shallow Convolutional Neural Network for COVID-19 Outbreak Screening using Chest X-rays

Cited by 25 publications

References 4 publications

Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019

Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019

A Survey on Deep Transfer Learning and Edge Computing for Mitigating the COVID-19 Pandemic

Data Analytics: COVID-19 Prediction Using Multimodal Data

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