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1. Exploring The Application of Artificial Intelligence And Machine Learning To Combat Covid-19 And Implication On Health Services.

Author

Mishra, Nirbhay Kumar, Gandhi, Savleen Singh, and Baba, Misha Hamid

Subjects
*MACHINE learning, *ARTIFICIAL intelligence, *MEDICAL care, *SARS-CoV-2, *COVID-19
Abstract

Novel coronavirus (COVID-19) pandemic, has raised a serious situation across world human population and has become serious threat as contagious outbreak. This paper aims to overview the recently intelligent systems based on Artificial Intelligence using different medical imaging modalities like Computer Tomography (CT) and X-ray. This paper specifically discusses the machine learning techniques developed for COVID-19 diagnosis and provides insights on well-known data sets used to train these AI based networks. It also highlights the use of AI in COVID detection and classification at faster process where normal COVID testing takes couple of days to produce. Finally, we conclude by addressing the challenges associated with the use of Machine Leaming methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways machine learning techniques are used and how they potentially further work in com batting the outbreak of CO VID-19. [ABSTRACT FROM AUTHOR]

Published
2022

2. An efficient method of detection of COVID-19 using Mask R-CNN on chest X-Ray images.

Author

Podder, Soumyajit, Bhattacharjee, Somnath, and Roy, Arijit

Subjects
*X-ray imaging, *COVID-19, *DEEP learning, *X-rays, *MEDICAL masks, *ARTIFICIAL intelligence, *DIAGNOSIS
Abstract

Artificial intelligence techniques are used on chest X-ray images for accurate detection of diseases and this paper aims to develop a process which is capable of diagnosing COVID-19 using deep learning methods on X-ray images. For this purpose, we used Mask R-CNN method to train and test on the dataset to classify between patients infected and non-infected with COVID-19. The dataset used here contains a large number of frontal views of X-ray images which are an essential resource for the algorithms used in the development of tools for the detection of COVID-19. Using 668 chest Xray images, the proposed model achieved an accuracy as high as 96.98%, specificity of 97.36% with the precision of 96.60%. The entire process is presented in detail. When a comparison table on the AIbased techniques is prepared, it is noticed that the Mask R-CNN technique on chest X-ray images provides better efficiency in the detection of COVID-19. The Mask R-CNN method is found to be accurate and robust in the detection of COVID-19 from chest X-ray images. [ABSTRACT FROM AUTHOR]

Published
2021
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4. A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

Author

Md. Milon Islam, Fakhri Karray, Jia Zeng, and Reda Alhajj

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, General Computer Science, Coronavirus disease 2019 (COVID-19), Computer science, diagnosis, Computer Vision and Pattern Recognition (cs.CV), Biomedical Engineering, Computer Science - Computer Vision and Pattern Recognition, medicine.disease_cause, Field (computer science), 030218 nuclear medicine & medical imaging, Machine Learning (cs.LG), 03 medical and health sciences, AI and IoT Convergence for Smart Health, 0302 clinical medicine, Taxonomy (general), Pandemic, deep transfer learning, medicine, FOS: Electrical engineering, electronic engineering, information engineering, General Materials Science, Electrical and Electronic Engineering, Systems, man, and cybernetics, 030304 developmental biology, Coronavirus, Computational and artificial intelligence, 0303 health sciences, business.industry, Deep learning, Image and Video Processing (eess.IV), General Engineering, Outbreak, COVID-19, deep learning, Electrical Engineering and Systems Science - Image and Video Processing, Data science, 3. Good health, TK1-9971, Categorization, x-ray, computer tomography, Artificial intelligence, Electrical engineering. Electronics. Nuclear engineering, business, Transfer of learning
Abstract

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways deep learning techniques are used in this regard and how they potentially further works in combatting the outbreak of COVID-19., Comment: 18 pages, 2 figures, 4 Tables

Published
2020
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5. An efficient machine learning-based COVID-19 identification utilizing chest X-ray images

Author

Mahmoud Masadeh, Ayah Masadeh, Omar Alshorman, Falak H Khasawneh, and Mahmoud Ali Masadeh

Subjects
X-ray, Information Systems and Management, Artificial Intelligence, Control and Systems Engineering, Convolutional neural, Diagnosis, Machine learning, COVID-19, Deep learning, Network, Electrical and Electronic Engineering
Abstract

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.

Published
2022