The Practicality of Deep Learning Algorithms in COVID-19 Detection: Application to Chest X-ray Images

Alorf, Abdulaziz

doi:10.3390/a14060183

Cited by 11 publications

(3 citation statements)

References 64 publications

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“…10,11 For example, Alorf et al developed an approach that uses VGG16 network 12 to detect COVID-19 in chest X-ray images. 13 Madhavan et al introduced an approach that uses ResNet-50 network 14 to extract features from chest X-ray images and detect COVID-19. 15 Khan et al developed an approach that extracts deep features from computed tomography images to predict COVID-19-pneumonia.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, deep convolutional neural network approaches have been used in detecting and classifying different virus‐related diseases 10,11 . For example, Alorf et al developed an approach that uses VGG16 network 12 to detect COVID‐19 in chest X‐ray images 13 . Madhavan et al introduced an approach that uses ResNet‐50 network 14 to extract features from chest X‐ray images and detect COVID‐19 15 .…”

Section: Introductionmentioning

confidence: 99%

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An effective human monkeypox classification using vision transformer

Aloraini

2023

Int J Imaging Syst Tech

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Monkeypox virus has become a popular disease as many people have been recently infected with this virus around the world. This virus causes fears of another global pandemic similar to COVID‐19 pandemic. Therefore, it is important to determine infected people at an early stage to reduce the spread of this virus. Creating an automatic approach to detect monkeypox virus is a challenging problem due to the similar appearance between skin infections. Currently, deep convolutional neural network approaches have been used in detecting and classifying skin infections. However, these approaches sever from certain challenges such as being unable to capture the global context and low accuracy rate. In this paper, we propose a novel approach based on vision transformer to automatically detect monkeypox virus in skin images. We fine‐tune vision transformer for the human monkeypox classification problems. This fine‐tuned vision transformer splits an image into image patches and then these patches are used as input to the transformer in a sequence structure, followed by added layers to detect monkeypox virus. The experimental results show that the proposed approach achieves excellent detection accuracy using a public dataset.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An effective human monkeypox classification using vision transformer

Aloraini

2023

Int J Imaging Syst Tech

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“…The experiment used Raspberry Pi Linux and Python code to perform a sequential feature selector. A similar work, documented in [ 22 ], implemented VGG16 and Xception to distinguish COVID-19 infections from noninfected cases. They developed 2 models using 1037 CXR images (402 COVID-19 images, 400 normal images, 200 pneumonia images, and 35 images without COVID-19 or pneumonia infection).…”

Section: Introductionmentioning

confidence: 99%

Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation

et al. 2023

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Background The COVID-19 pandemic has raised global concern, with moderate to severe cases displaying lung inflammation and respiratory failure. Chest x-ray (CXR) imaging is crucial for diagnosis and is usually interpreted by experienced medical specialists. Machine learning has been applied with acceptable accuracy, but computational efficiency has received less attention. Objective We introduced a novel hybrid machine learning model to accurately classify COVID-19, non-COVID-19, and healthy patients from CXR images with reduced computational time and promising results. Our proposed model was thoroughly evaluated and compared with existing models. Methods A retrospective study was conducted to analyze 5 public data sets containing 4200 CXR images using machine learning techniques including decision trees, support vector machines, and neural networks. The images were preprocessed to undergo image segmentation, enhancement, and feature extraction. The best performing machine learning technique was selected and combined into a multilayer hybrid classification model for COVID-19 (MLHC-COVID-19). The model consisted of 2 layers. The first layer was designed to differentiate healthy individuals from infected patients, while the second layer aimed to classify COVID-19 and non-COVID-19 patients. Results The MLHC-COVID-19 model was trained and evaluated on unseen COVID-19 CXR images, achieving reasonably high accuracy and F measures of 0.962 and 0.962, respectively. These results show the effectiveness of the MLHC-COVID-19 in classifying COVID-19 CXR images, with improved accuracy and a reduction in interpretation time. The model was also embedded into a web-based MLHC-COVID-19 computer-aided diagnosis system, which was made publicly available. Conclusions The study found that the MLHC-COVID-19 model effectively differentiated CXR images of COVID-19 patients from those of healthy and non-COVID-19 individuals. It outperformed other state-of-the-art deep learning techniques and showed promising results. These results suggest that the MLHC-COVID-19 model could have been instrumental in early detection and diagnosis of COVID-19 patients, thus playing a significant role in controlling and managing the pandemic. Although the pandemic has slowed down, this model can be adapted and utilized for future similar situations. The model was also integrated into a publicly accessible web-based computer-aided diagnosis system.

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