Using Deep Learning Algorithms in Chest X-ray Image COVID-19 Diagnosis

Chang, Yu-Chih; Liu, An-Shun; Chu, Woei-Chyn

doi:10.1109/ecbios51820.2021.9510393

Cited by 2 publications

(3 citation statements)

References 1 publication

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“…In [14] [15] exp lains the use of an ensemble model for detection of COVID and uses ensemble and deep learning techniques to mitigate the using snapshots of the models proposed in [16] and a weighted average type ensembling technique taking care of the different sensitivities of the models. [17] uses the VGG-16 based model for COVID-19 detection. [18] proposes a novel CNN architecture which has accuracy of 99.02%.…”

Section: Use Of Cnn For Covid-19 Detectionmentioning

confidence: 99%

A Survey on Deep Learning Techniques for COVID-19 Detection using Medical Imaging

Jain

Singh

Rana

et al. 2022

2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS)

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Covid-19 has disrupted lives throughout the world. It has spread all over the world and detection of the virus is an imperative step in beating the virus. Methods such as the RTPCR and Rapid antigen tests are not only time consuming but also complex and expensive. Since the virus attacks the lungs, the Xray images of the chest can be used for the detection of coronavirus. This paper summarizes as well as gives a detailed study of the research and various techniques used for this subject. Methods used for COVID-19 detection using medical imaging using Chest X-Ray (CXR) and CT scan images as well as role and usage of GANs in tackling this problem have been summarized.

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Section: Use Of Cnn For Covid-19 Detectionmentioning

confidence: 99%

A Survey on Deep Learning Techniques for COVID-19 Detection using Medical Imaging

Jain

Singh

Rana

et al. 2022

2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS)

View full text Add to dashboard Cite

show abstract

“…Keeping in view the reality that AI techniques and models have left no stone unturned, Togaçar et al Ismail and Sengür assumed deep learning models for the prediction of COVID-19 detection (4). Chang et al (5) presented deep learning for diagnosing COVID-19 infection using chest X-ray images. Karhan and Akal presented a convolutional neural network (CNN)-based architecture for the identification of COVID-19 using X-ray images (6).…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the number of images used for experimental purposes is much smaller than a standard testing and validation model. Similarly, Chang et al(5) have proposed VGG16 for diagnosing COVID-19 infection using chest X-ray images. An overall accuracy rate of 78% is achieved for this model, which is comparatively much smaller and ultimately it reflects a high misclassification rate.…”

mentioning

confidence: 99%

A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection

Khan

Nazir

2022

Front. Public Health

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Recently, the novel coronavirus disease 2019 (COVID-19) has posed many challenges to the research community by presenting grievous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in a huge number of mortalities and high morbidities worldwide. Furthermore, the symptoms-based variations in virus type add new challenges for the research and practitioners to combat. COVID-19-infected patients comprise trenchant radiographic visual features, including dry cough, fever, dyspnea, fatigue, etc. Chest X-ray is considered a simple and non-invasive clinical adjutant that performs a key role in the identification of these ocular responses related to COVID-19 infection. Nevertheless, the defined availability of proficient radiologists to understand the X-ray images and the elusive aspects of disease radiographic replies to remnant the biggest bottlenecks in manual diagnosis. To address these issues, the proposed research study presents a hybrid deep learning model for the accurate diagnosing of Delta-type COVID-19 infection using X-ray images. This hybrid model comprises visual geometry group 16 (VGG16) and a support vector machine (SVM), where the VGG16 is accustomed to the identification process, while the SVM is used for the severity-based analysis of the infected people. An overall accuracy rate of 97.37% is recorded for the assumed model. Other performance metrics such as the area under the curve (AUC), precision, F-score, misclassification rate, and confusion matrix are used for validation and analysis purposes. Finally, the applicability of the presumed model is assimilated with other relevant techniques. The high identification rates shine the applicability of the formulated hybrid model in the targeted research domain.

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Using Deep Learning Algorithms in Chest X-ray Image COVID-19 Diagnosis

Cited by 2 publications

References 1 publication

A Survey on Deep Learning Techniques for COVID-19 Detection using Medical Imaging

A Survey on Deep Learning Techniques for COVID-19 Detection using Medical Imaging

A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection

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