An Artificial Intelligence Based Technique for COVID-19 Diagnosis from Chest X-Ray

Bekhet, Saddam; Hassaballah, M.; Kenk, Mourad A.; Hameed, Mohamed Abdel

doi:10.1109/niles50944.2020.9257930

Cited by 34 publications

(18 citation statements)

References 23 publications

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“…In a different study [28], an X-ray image dataset with 9 different types of pneumonia infections of size 316 scans (where 253 were of COVID-19 patients) was considered. Following a hyper-parameter tuning phase of the considered CNN-based model, an accuracy performance of 96% was achieved in detecting the COVID-19 cases from the non-COVID-19 ones.…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques

et al. 2021

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It became apparent that mankind has to learn to live with and adapt to COVID-19, especially because the developed vaccines thus far do not prevent the infection but rather just reduce the severity of the symptoms. The manual classification and diagnosis of COVID-19 pneumonia requires specialized personnel and is time consuming and very costly. On the other hand, automatic diagnosis would allow for real-time diagnosis without human intervention resulting in reduced costs. Therefore, the objective of this research is to propose a novel optimized Deep Learning (DL) approach for the automatic classification and diagnosis of COVID-19 pneumonia using X-ray images. For this purpose, a publicly available dataset of chest X-rays on Kaggle was used in this study. The dataset was developed over three stages in a quest to have a unified COVID-19 entities dataset available for researchers. The dataset consists of 21,165 anterior-to-posterior and posterior-to-anterior chest X-ray images classified as: Normal (48%), COVID-19 (17%), Lung Opacity (28%) and Viral Pneumonia (6%). Data Augmentation was also applied to increase the dataset size to enhance the reliability of results by preventing overfitting. An optimized DL approach is implemented in which chest X-ray images go through a three-stage process. Image Enhancement is performed in the first stage, followed by Data Augmentation stage and in the final stage the results are fed to the Transfer Learning algorithms (AlexNet, GoogleNet, VGG16, VGG19, and DenseNet) where the images are classified and diagnosed. Extensive experiments were performed under various scenarios, which led to achieving the highest classification accuracy of 95.63% through the application of VGG16 transfer learning algorithm on the augmented enhanced dataset with freeze weights. This accuracy was found to be better as compared to the results reported by other methods in the recent literature. Thus, the proposed approach proved superior in performance as compared with that of other similar approaches in the extant literature, and it made a valuable contribution to the body of knowledge. Although the results achieved so far are promising, further work is planned to correlate the results of the proposed approach with clinical observations to further enhance the efficiency and accuracy of COVID-19 diagnosis.

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Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques

et al. 2021

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“…Developers and physicists are focusing on technologies focused on neural networks that can evaluate accessible large data for symptoms of disease until humans can even identify them more accurately and effectively. In such systems people need to train the neural networks to locate the data in the database [30].…”

Section: A Device Fo R Identificationmentioning

confidence: 99%

The Avoidance And Detection Function Of Artificial Intelligence In Covid-19

Abdulateef¹,

Mohammed

Abdulateef

2021

2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)

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The whole planet today is having to fight COVID-19 with big obstacles. The COVID-19 influenced several countries around the world between December 2019 and the present day. Many organisations and scientists seek to find a vaccine and to minimize the spread of COVID-19. Artificial Intelligence is one technology that can successfully address this virus (AI). In the case of other pathogens, artificial intelligence performed very well and could help us cope with the virus COVID-19, too. It is the imagination and the information of the people who use it which will help to overcome this dilemma. In some previous instances AI played a major role in virus prevention and identification. We have an ability to detect certain aspects of the AI because of the COVID-19 crisis. Machine learning that an AI subclass is used to identify patterns and to plan valuable knowledge based on recorded data sets. At the point where used entirely, AI can exceed human efforts by speed and differentiate designs from knowledge previously ignored. However many correct and appropriate data are needed for effective implementation of AI systems. This paper discusses the AI's role in COVID-19 prevention and detection and examines numerous technological aspects of AI. This paper would also clarify where AI will contribute with likely solutions to stop the spread of COVID-19.

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“…12 AI can analyse the heart's anatomy, monitor the appropriate segmentation of the cardiac MRI ventricle, detect arrhythmias analyse heart imaging, blood pressure, oxygen saturation, heart rate and also predicted a heart attack. [12][13][14][15][16][17][18]…”

Section: Aimentioning

confidence: 99%

A review of emerging innovations in COVID-19

Saggi¹

2021

Int J Community Med Public Health

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The COVID-19 pandemic has globally impacted humanity. Human health, productivity, social life and function is affected. Every country has felt effects, domestic as well as international. Emerging technologies also known as disruptive technologies have played a significant role in the pandemic. This literature review is a manifest overview on the utilization of existing technologies during the COVID-19 pandemic. The strengths, weakness, opportunities and threats of the innovative technologies under review have been summarized. Their benefits and further scope of disruptive innovations have been reviewed. The review aims to identify and highlight approaches/gaps for improvement and future application.

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An Artificial Intelligence Based Technique for COVID-19 Diagnosis from Chest X-Ray

Cited by 34 publications

References 23 publications

COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques

COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques

The Avoidance And Detection Function Of Artificial Intelligence In Covid-19

A review of emerging innovations in COVID-19

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