Detection of COVID-19 Disease in Chest X-Ray Images with capsul networks: application with cloud computing

Aksoy, Bekir; Salman, Osamah Khaled Musleh

doi:10.1080/0952813x.2021.1908431

Cited by 9 publications

(8 citation statements)

References 32 publications

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“…This is clear indication of the least trade-off between sensitivity and specificity of the model and can be considered as a good grade of classification. This is also considered to be ‘outstanding’ in the domain of medical disease diagnosis [33] , [56] .…”

Section: Resultsmentioning

confidence: 99%

“…The experimental results indicate the capability of the CT-CAPS to automatically analyze volumetric chest CT scans and distinguish different cases with the accuracy of 90.8%, sensitivity of 94.5%, and specificity of 86.0%. Aksoy and Salman [33] analysed chest X-ray images of 1019 patients using Capsule Networks (CapsNet) model, designed can detect COVID-19 disease with an accuracy rate of 98.02%. Saha et al [34] proposed GraphCovidNet model to detect COVID-19 from CT-scans and CXRs of the afected patients & evaluated this model on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-ray Images (Pneumonia) dataset and CMSC-678-MLProject dataset.…”

Section: Related Work On Ai Approaches For Covid-19 Detectionmentioning

confidence: 99%

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COVID-WideNet—A capsule network for COVID-19 detection

Gupta

Siddiqui²,

Huang³

et al. 2022

Applied Soft Computing

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Section: Resultsmentioning

confidence: 99%

Section: Related Work On Ai Approaches For Covid-19 Detectionmentioning

confidence: 99%

COVID-WideNet—A capsule network for COVID-19 detection

Gupta

Siddiqui²,

Huang³

et al. 2022

Applied Soft Computing

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Section: Literature Surveymentioning

confidence: 99%

“…32 Aksoy and Salman obtained 98.02% accuracy in the classification study they carried out using CapsNet architecture on the dataset consisting of CXR images of 510 healthy and 509 COVID-19 patients. 33 Ravi et al mentioned that small-sized datasets were used in most of the deep learning-based studies carried out for the diagnosis of COVID-19 in the literature. They emphasized that although successful results were obtained in these studies, the generalizability was low due to the limited dataset.…”

Section: Literature Surveymentioning

confidence: 99%

A deep learning‐based approach for diagnosing COVID‐19 on chest x‐ray images, and a test study with clinical experts

Sevli

2022

Computational Intelligence

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Pneumonia is among the common symptoms of the virus that causes COVID‐19, which has turned into a worldwide pandemic. It is possible to diagnose pneumonia by examining chest radiographs. Chest x‐ray (CXR) is a fast, low‐cost, and practical method widely used in this field. The fact that different pathogens other than COVID‐19 also cause pneumonia and the radiographic images of all are similar make it difficult to detect the source of the disease. In this study, automatic detection of COVID‐19 cases over CXR images was tried to be performed using convolutional neural network (CNN), a deep learning technique. Classifications were carried out using six different architectures on the dataset consisting of 15,153 images of three different types: healthy, COVID‐19, and other viral‐induced pneumonia. In the classifications performed with five different state‐of‐art models, ResNet18, GoogLeNet, AlexNet, VGG16, and DenseNet161, and a minimal CNN architecture specific to this study, the most successful result was obtained with the ResNet18 architecture as 99.25% accuracy. Although the minimal CNN model developed for this study has a simpler structure, it was observed that it has a success to compete with more complex models. The performances of the models used in this study were compared with similar studies in the literature and it was revealed that they generally achieved higher success. The model with the highest success was transformed into a test application, tested by 10 volunteer clinicians, and it was concluded that it provides 99.06% accuracy in practical use. This result reveals that the conducted study can play the role of a successful decision support system for experts.

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“…An efficient machine learning-based covid-19 identification utilizing chest x-ray … (Mahmoud Masadeh) 357 100% vaccinated world is a long-journey, specially for low-income countries, where the best solution is COVID-19 avoidance and early detection [4]. Since the beginning of the pandemic, different repositories, e.g., Github and Kaggle, presented online chest X-ray images for natural and infected peoples where such images include notable knowledge regarding the COVID-19 virus [5]. The diagnosing process of COVID-19 required the direct contact of the infected patients with the medical staff, which is very risky task [6].…”

Section: Introductionmentioning

confidence: 99%

An efficient machine learning-based COVID-19 identification utilizing chest X-ray images

Masadeh

AlShorman

et al. 2022

IJ-AI

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There is no well-known vaccine for coronavirus disease (COVID-19) with 100% efficiency. COVID-19 patients suffer from a lung infection, where lung-related problems can be effectively diagnosed with image techniques. The golden test for COVID-19 diagnosis is the RT-PCR test, which is costly, time-consuming and unavailable for various countries. Thus, machine learning-based tools are a viable solution. Here, we used a labelled chest X-ray of three categories, then performed data cleaning and augmentation to use the data in deep learning-based convolutional neural network (CNN) models. We compared the performance of different models that we gradually built and analyzed their accuracy. For that, we used 2905 chest X-ray scan samples. We were able to develop a model with the best accuracy of 97.44% for identifying COVID-19 using X-ray images. Thus, in this paper, we attested the feasibility of efficiently applying machine learning (ML) based models for medical image classification.

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Detection of COVID-19 Disease in Chest X-Ray Images with capsul networks: application with cloud computing

Cited by 9 publications

References 32 publications

COVID-WideNet—A capsule network for COVID-19 detection

COVID-WideNet—A capsule network for COVID-19 detection

A deep learning‐based approach for diagnosing COVID‐19 on chest x‐ray images, and a test study with clinical experts

An efficient machine learning-based COVID-19 identification utilizing chest X-ray images

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