501
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Sakib S, Tazrin T, Fouda MM, Fadlullah ZM, Guizani M. DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:171575-171589. [PMID: 34976555 PMCID: PMC8675549 DOI: 10.1109/access.2020.3025010] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 08/26/2020] [Indexed: 05/05/2023]
Abstract
With the exponentially growing COVID-19 (coronavirus disease 2019) pandemic, clinicians continue to seek accurate and rapid diagnosis methods in addition to virus and antibody testing modalities. Because radiographs such as X-rays and computed tomography (CT) scans are cost-effective and widely available at public health facilities, hospital emergency rooms (ERs), and even at rural clinics, they could be used for rapid detection of possible COVID-19-induced lung infections. Therefore, toward automating the COVID-19 detection, in this paper, we propose a viable and efficient deep learning-based chest radiograph classification (DL-CRC) framework to distinguish the COVID-19 cases with high accuracy from other abnormal (e.g., pneumonia) and normal cases. A unique dataset is prepared from four publicly available sources containing the posteroanterior (PA) chest view of X-ray data for COVID-19, pneumonia, and normal cases. Our proposed DL-CRC framework leverages a data augmentation of radiograph images (DARI) algorithm for the COVID-19 data by adaptively employing the generative adversarial network (GAN) and generic data augmentation methods to generate synthetic COVID-19 infected chest X-ray images to train a robust model. The training data consisting of actual and synthetic chest X-ray images are fed into our customized convolutional neural network (CNN) model in DL-CRC, which achieves COVID-19 detection accuracy of 93.94% compared to 54.55% for the scenario without data augmentation (i.e., when only a few actual COVID-19 chest X-ray image samples are available in the original dataset). Furthermore, we justify our customized CNN model by extensively comparing it with widely adopted CNN architectures in the literature, namely ResNet, Inception-ResNet v2, and DenseNet that represent depth-based, multi-path-based, and hybrid CNN paradigms. The encouragingly high classification accuracy of our proposal implies that it can efficiently automate COVID-19 detection from radiograph images to provide a fast and reliable evidence of COVID-19 infection in the lung that can complement existing COVID-19 diagnostics modalities.
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Affiliation(s)
- Sadman Sakib
- Department of Computer ScienceLakehead UniversityThunder BayONP7B 5E1Canada
| | - Tahrat Tazrin
- Department of Computer ScienceLakehead UniversityThunder BayONP7B 5E1Canada
| | - Mostafa M. Fouda
- Department of Electrical and Computer EngineeringCollege of Science and EngineeringIdaho State UniversityPocatelloID83209USA
- Department of Electrical EngineeringFaculty of Engineering at ShoubraBenha UniversityCairo11629Egypt
| | - Zubair Md. Fadlullah
- Department of Computer ScienceLakehead UniversityThunder BayONP7B 5E1Canada
- Thunder Bay Regional Health Research Institute (TBRHRI)Thunder BayONP7B 7A5Canada
| | - Mohsen Guizani
- Department of Computer Science and EngineeringCollege of EngineeringQatar UniversityDohaQatar
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502
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Gomes JC, Barbosa VADF, Santana MA, Bandeira J, Valença MJS, de Souza RE, Ismael AM, dos Santos WP. IKONOS: an intelligent tool to support diagnosis of COVID-19 by texture analysis of X-ray images. RESEARCH ON BIOMEDICAL ENGINEERING 2020. [PMCID: PMC7471577 DOI: 10.1007/s42600-020-00091-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Purpose In late 2019, the SARS-CoV-2 virus spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. The standard diagnostic method for pneumonia is chest X-ray image. There are many advantages to using COVID-19 diagnostic X-rays: low cost, fast, and widely available. Methods We propose an intelligent system to support diagnosis by X-ray images. We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Results Support vector machines stood out, reaching an average accuracy of 89.78%, average sensitivity of 0.8979, and average precision and specificity of 0.8985 and 0.9963, respectively. Conclusion Using features based on textures and shapes combined with classical classifiers, the developed system was able to differentiate COVID-19 from viral and bacterial pneumonia with low computational cost.
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Affiliation(s)
- Juliana C. Gomes
- Polytechnique School of the University of Pernambuco, Recife, Brazil
| | | | - Maíra A. Santana
- Polytechnique School of the University of Pernambuco, Recife, Brazil
| | - Jonathan Bandeira
- Polytechnique School of the University of Pernambuco, Recife, Brazil
| | | | | | - Aras Masood Ismael
- Information Technology Department, Technical College of Informatics, Sulaimani Polytechnic University, Sulaymaniyah, Iraq
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503
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Swapnarekha H, Behera HS, Nayak J, Naik B. Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review. CHAOS, SOLITONS, AND FRACTALS 2020; 138:109947. [PMID: 32836916 PMCID: PMC7256553 DOI: 10.1016/j.chaos.2020.109947] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 05/26/2020] [Indexed: 05/09/2023]
Abstract
The World Health Organization (WHO) declared novel coronavirus 2019 (COVID-19), an infectious epidemic caused by SARS-CoV-2, as Pandemic in March 2020. It has affected more than 40 million people in 216 countries. Almost in all the affected countries, the number of infected and deceased patients has been enhancing at a distressing rate. As the early prediction can reduce the spread of the virus, it is highly desirable to have intelligent prediction and diagnosis tools. The inculcation of efficient forecasting and prediction models may assist the government in implementing better design strategies to prevent the spread of virus. In this paper, a state-of-the-art analysis of the ongoing machine learning (ML) and deep learning (DL) methods in the diagnosis and prediction of COVID-19 has been done. Moreover, a comparative analysis on the impact of machine learning and other competitive approaches like mathematical and statistical models on COVID-19 problem has been conducted. In this study, some factors such as type of methods(machine learning, deep learning, statistical & mathematical) and the impact of COVID research on the nature of data used for the forecasting and prediction of pandemic using computing approaches has been presented. Finally some important research directions for further research on COVID-19 are highlighted which may facilitate the researchers and technocrats to develop competent intelligent models for the prediction and forecasting of COVID-19 real time data.
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Affiliation(s)
- H Swapnarekha
- Department of Information Technology, Veer Surendra Sai University of Technology (VSSUT), Burla, Sambalpur-768018, Odisha, India
| | - Himansu Sekhar Behera
- Department of Information Technology, Veer Surendra Sai University of Technology (VSSUT), Burla, Sambalpur-768018, Odisha, India
| | - Janmenjoy Nayak
- Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), Tekkali, Andhra Pradesh 532201, India
| | - Bighnaraj Naik
- Department of Computer Application, Veer Surendra Sai University of Technology (VSSUT), Burla, Sambalpur-768018, Odisha, India
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504
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Ahuja S, Panigrahi BK, Dey N, Rajinikanth V, Gandhi TK. Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. APPL INTELL 2020; 51:571-585. [PMID: 34764547 PMCID: PMC7440966 DOI: 10.1007/s10489-020-01826-w] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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505
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Zhou L, Li Z, Zhou J, Li H, Chen Y, Huang Y, Xie D, Zhao L, Fan M, Hashmi S, Abdelkareem F, Eiada R, Xiao X, Li L, Qiu Z, Gao X. A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2638-2652. [PMID: 32730214 PMCID: PMC8769013 DOI: 10.1109/tmi.2020.3001810] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 06/06/2020] [Accepted: 06/07/2020] [Indexed: 05/03/2023]
Abstract
COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid detection has been mainly used as the gold standard to confirm this RNA virus-based disease, it has been shown that such a strategy has a high false negative rate, especially for patients in the early stage, and thus CT imaging has been applied as a major diagnostic modality in confirming positive COVID-19. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this paper, we propose a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon two innovations: 1) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients' data measured at different time points, which greatly alleviates the data scarcity issue; and 2) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.
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Affiliation(s)
- Longxi Zhou
- Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC)King Abdullah University of Science and Technology (KAUST)Thuwal23955Saudi Arabia
| | - Zhongxiao Li
- Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC)King Abdullah University of Science and Technology (KAUST)Thuwal23955Saudi Arabia
| | - Juexiao Zhou
- Department of BiologySouthern University of Science and TechnologyShenzhen518055China
| | - Haoyang Li
- Cancer Systems Biology Center, China–Japan Union HospitalJilin UniversityChangchun130031China
| | | | - Yuxin Huang
- Heilongjiang Tuomeng Technology Company Ltd.Harbin150040China
| | - Dexuan Xie
- Department of Computer TomographyThe First Affiliated Hospital of Harbin Medical UniversityHarbin150001China
| | - Lintao Zhao
- Department of Computer TomographyThe First Hospital of Harbin Medical UniversityHarbin150010China
| | - Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi UniversityHangzhou310018China
| | - Shahrukh Hashmi
- Oncology CenterKing Faisal Specialist Hospital and Research CenterRiyadh11211Saudi Arabia
| | - Faisal Abdelkareem
- Department Medical ImagingKing Faisal Specialist Hospital and Research CenterRiyadh11211Saudi Arabia
| | - Riham Eiada
- Department Medical ImagingKing Faisal Specialist Hospital and Research CenterRiyadh11211Saudi Arabia
| | - Xigang Xiao
- Department of Computer TomographyThe First Affiliated Hospital of Harbin Medical UniversityHarbin150001China
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi UniversityHangzhou310018China
| | - Zhaowen Qiu
- Institute of Information and Computer Engineering, Northeast Forestry UniversityHarbin150040China
| | - Xin Gao
- Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC)King Abdullah University of Science and Technology (KAUST)Thuwal23955Saudi Arabia
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506
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Tsikala Vafea M, Atalla E, Georgakas J, Shehadeh F, Mylona EK, Kalligeros M, Mylonakis E. Emerging Technologies for Use in the Study, Diagnosis, and Treatment of Patients with COVID-19. Cell Mol Bioeng 2020; 13:249-257. [PMID: 32837582 PMCID: PMC7314428 DOI: 10.1007/s12195-020-00629-w] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 06/18/2020] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION The COVID-19 pandemic has caused an unprecedented health and economic worldwide crisis. Innovative solutions are imperative given limited resources and immediate need for medical supplies, healthcare support and treatments. AIM The purpose of this review is to summarize emerging technologies being implemented in the study, diagnosis, and treatment of COVID-19. RESULTS Key focus areas include the applications of artificial intelligence, the use of Big Data and Internet of Things, the importance of mathematical modeling for predictions, utilization of technology for community screening, the use of nanotechnology for treatment and vaccine development, the utility of telemedicine, the implementation of 3D-printing to manage new demands and the potential of robotics. CONCLUSION The review concludes by highlighting the need for collaboration in the scientific community with open sharing of knowledge, tools, and expertise.
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Affiliation(s)
- Maria Tsikala Vafea
- Infectious Diseases Division, Rhode Island Hospital, Warren Alpert Medical School of Brown University, 593 Eddy Street, POB, 3rd Floor, Suite 328/330, Providence, RI 02903 USA
| | - Eleftheria Atalla
- Infectious Diseases Division, Rhode Island Hospital, Warren Alpert Medical School of Brown University, 593 Eddy Street, POB, 3rd Floor, Suite 328/330, Providence, RI 02903 USA
| | - Joanna Georgakas
- Infectious Diseases Division, Rhode Island Hospital, Warren Alpert Medical School of Brown University, 593 Eddy Street, POB, 3rd Floor, Suite 328/330, Providence, RI 02903 USA
| | - Fadi Shehadeh
- Infectious Diseases Division, Rhode Island Hospital, Warren Alpert Medical School of Brown University, 593 Eddy Street, POB, 3rd Floor, Suite 328/330, Providence, RI 02903 USA
| | - Evangelia K. Mylona
- Infectious Diseases Division, Rhode Island Hospital, Warren Alpert Medical School of Brown University, 593 Eddy Street, POB, 3rd Floor, Suite 328/330, Providence, RI 02903 USA
| | - Markos Kalligeros
- Infectious Diseases Division, Rhode Island Hospital, Warren Alpert Medical School of Brown University, 593 Eddy Street, POB, 3rd Floor, Suite 328/330, Providence, RI 02903 USA
| | - Eleftherios Mylonakis
- Infectious Diseases Division, Rhode Island Hospital, Warren Alpert Medical School of Brown University, 593 Eddy Street, POB, 3rd Floor, Suite 328/330, Providence, RI 02903 USA
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507
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Pham QV, Nguyen DC, Huynh-The T, Hwang WJ, Pathirana PN. Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:130820-130839. [PMID: 34812339 DOI: 10.13140/rg.2.2.23518.38727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 07/11/2020] [Indexed: 05/24/2023]
Abstract
The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.
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Affiliation(s)
- Quoc-Viet Pham
- Research Institute of Computer, Information and CommunicationPusan National University Busan 46241 South Korea
| | - Dinh C Nguyen
- School of EngineeringDeakin University Waurn Ponds VIC 3216 Australia
| | - Thien Huynh-The
- ICT Convergence Research CenterKumoh National Institute of Technology Gumi 39177 South Korea
| | - Won-Joo Hwang
- Department of Biomedical Convergence EngineeringPusan National University Busan 46241 South Korea
- Department of Information Convergence Engineering (Artificial Intelligence)Pusan National University Busan 46241 South Korea
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508
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Pham QV, Nguyen DC, Huynh-The T, Hwang WJ, Pathirana PN. Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:130820-130839. [PMID: 34812339 PMCID: PMC8545324 DOI: 10.1109/access.2020.3009328] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 07/11/2020] [Indexed: 05/18/2023]
Abstract
The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.
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Affiliation(s)
- Quoc-Viet Pham
- Research Institute of Computer, Information and CommunicationPusan National UniversityBusan46241South Korea
| | - Dinh C. Nguyen
- School of EngineeringDeakin UniversityWaurn PondsVIC3216Australia
| | - Thien Huynh-The
- ICT Convergence Research CenterKumoh National Institute of TechnologyGumi39177South Korea
| | - Won-Joo Hwang
- Department of Biomedical Convergence EngineeringPusan National UniversityBusan46241South Korea
- Department of Information Convergence Engineering (Artificial Intelligence)Pusan National UniversityBusan46241South Korea
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509
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Singh D, Kumar V, Kaur M. Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks. Eur J Clin Microbiol Infect Dis 2020. [PMID: 32337662 DOI: 10.1007/s10096-020-03901] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.
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Affiliation(s)
- Dilbag Singh
- Computer Science and Engineering Department, School of Computing and Information Technology, Manipal University Jaipur, Jaipur, India
| | - Vijay Kumar
- Computer Science and Engineering Department, National Institute of Technology, Hamirpur, Himachal Pradesh, India
| | - Manjit Kaur
- Computer and Communication Engineering Department, School of Computing and Information Technology, Manipal University Jaipur, Jaipur, India.
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510
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Kumar A, Gupta PK, Srivastava A. A review of modern technologies for tackling COVID-19 pandemic. Diabetes Metab Syndr 2020; 14:569-573. [PMID: 32413821 PMCID: PMC7204706 DOI: 10.1016/j.dsx.2020.05.008] [Citation(s) in RCA: 127] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 05/03/2020] [Accepted: 05/04/2020] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Science and technology sector constituting of data science, machine learning and artificial intelligence are contributing towards COVID-19. The aim of the present study is to discuss the various aspects of modern technology used to fight against COVID-19 crisis at different scales, including medical image processing, disease tracking, prediction outcomes, computational biology and medicines. METHODS A progressive search of the database related to modern technology towards COVID-19 is made. Further, a brief review is done on the extracted information by assessing the various aspects of modern technologies for tackling COVID-19 pandemic. RESULTS We provide a window of thoughts on review of the technology advances used to decrease and smother the substantial impact of the outburst. Though different studies relating to modern technology towards COVID-19 have come up, yet there are still constrained applications and contributions of technology in this fight. CONCLUSIONS On-going progress in the modern technology has contributed in improving people's lives and hence there is a solid conviction that validated research plans including artificial intelligence will be of significant advantage in helping people to fight this infection.
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Affiliation(s)
- Aishwarya Kumar
- ICFAI Business School, The ICFAI University, Dehradun, Uttarakhand, India.
| | - Puneet Kumar Gupta
- ICFAI Business School, The ICFAI University, Dehradun, Uttarakhand, India.
| | - Ankita Srivastava
- ICFAI Business School, The ICFAI University, Dehradun, Uttarakhand, India.
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511
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Truncated inception net: COVID-19 outbreak screening using chest X-rays. Phys Eng Sci Med 2020; 43:915-925. [PMID: 32588200 PMCID: PMC7315909 DOI: 10.1007/s13246-020-00888-x] [Citation(s) in RCA: 120] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 06/11/2020] [Indexed: 12/23/2022]
Abstract
Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in a short period of time, and the infection, caused by SARS-CoV-2, is spreading rapidly. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID-19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis, and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using the acquired CXRs, and proves the viability of using the proposed Truncated Inception Net as a screening tool.
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512
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Nandy Chatterjee T, Bandyopadhyay R. A Molecularly Imprinted Polymer-Based Technology for Rapid Testing of COVID-19. TRANSACTIONS OF THE INDIAN NATIONAL ACADEMY OF ENGINEERING : AN INTERNATIONAL JOURNAL OF ENGINEERING AND TECHNOLOGY 2020; 5:225-228. [PMID: 38624404 PMCID: PMC7293430 DOI: 10.1007/s41403-020-00125-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 06/03/2020] [Accepted: 06/09/2020] [Indexed: 12/24/2022]
Abstract
The outbreak of COVID-19 has taken a large number of lives since 2019 and the death toll continues to increase all over the world. Recent data reports that about 27 lacs of people are infected with this virus till date and around 2 lacs are dead due to this pandemic. The situation in India is no way better. In India, almost all the states have become victim of this deadly pandemic. Considering the enormous population in India, citizens here are facing acute shortage of detection kits and many are dying even before the knowledge of their infection. The present treatise proposes a molecularly imprinted polymer (MIP) based technique for simple and rapid detection of COVID-19. The technique will be inexpensive, selective, reusable and easy to handle. It has been already implemented in our laboratory in order to detect the taste contributing agents found in tea. This article discusses the detailed methodology and the resultant analytical characteristic of the sensors developed so far and also outlines the suitability of the MIP technique towards fabrication of testing kits for rapid detection of COVID-19.
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Affiliation(s)
- Trisita Nandy Chatterjee
- Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt Lake Campus, Block LB, Sector III, Plot 8, Salt Lake, Kolkata, 700 098 India
| | - Rajib Bandyopadhyay
- Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt Lake Campus, Block LB, Sector III, Plot 8, Salt Lake, Kolkata, 700 098 India
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513
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Sreepadmanabh M, Sahu AK, Chande A. COVID-19: Advances in diagnostic tools, treatment strategies, and vaccine development. J Biosci 2020; 45:148. [PMID: 33410425 PMCID: PMC7683586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 10/15/2020] [Indexed: 09/18/2023]
Abstract
An unprecedented worldwide spread of the SARS-CoV-2 has imposed severe challenges on healthcare facilities and medical infrastructure. The global research community faces urgent calls for the development of rapid diagnostic tools, effective treatment protocols, and most importantly, vaccines against the pathogen. Pooling together expertise across broad domains to innovate effective solutions is the need of the hour. With these requirements in mind, in this review, we provide detailed critical accounts on the leading efforts at developing diagnostics tools, therapeutic agents, and vaccine candidates. Importantly, we furnish the reader with a multidisciplinary perspective on how conventional methods like serology and RT-PCR, as well as cutting-edge technologies like CRISPR/Cas and artificial intelligence/machine learning, are being employed to inform and guide such investigations. We expect this narrative to serve a broad audience of both active and aspiring researchers in the field of biomedical sciences and engineering and help inspire radical new approaches towards effective detection, treatment, and prevention of this global pandemic.
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Affiliation(s)
- M Sreepadmanabh
- Molecular Virology Laboratory, Indian Institute of Science Education and Research, Bhopal, India
| | - Amit Kumar Sahu
- Molecular Virology Laboratory, Indian Institute of Science Education and Research, Bhopal, India
| | - Ajit Chande
- Molecular Virology Laboratory, Indian Institute of Science Education and Research, Bhopal, India
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514
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Özkaya U, Öztürk Ş, Barstugan M. Coronavirus (COVID-19) Classification Using Deep Features Fusion and Ranking Technique. STUDIES IN BIG DATA 2020. [DOI: 10.1007/978-3-030-55258-9_17] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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515
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Rahaman MM, Li C, Yao Y, Kulwa F, Rahman MA, Wang Q, Qi S, Kong F, Zhu X, Zhao X. Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020; 28:821-839. [PMID: 32773400 PMCID: PMC7592691 DOI: 10.3233/xst-200715] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/29/2020] [Accepted: 07/11/2020] [Indexed: 05/18/2023]
Abstract
BACKGROUND The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system. OBJECTIVE One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images. METHODS Due to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task. RESULTS A total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, 0.90, respectively. CONCLUSION This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.
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Affiliation(s)
- Md Mamunur Rahaman
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
| | - Frank Kulwa
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | | | - Qian Wang
- Liaoning Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - Shouliang Qi
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Fanjie Kong
- Electrical Engineering Department, Pratt School of Engineering Duke University, Durham, NC, USA
| | - Xuemin Zhu
- Whiting School of Engineering, Johns Hopkins University, 500 W University Parkway, MD, USA, USA
| | - Xin Zhao
- Environmental Engineering Department, Northeastern University, Shenyang, China
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516
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Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks. Eur J Clin Microbiol Infect Dis 2020; 39. [PMID: 32337662 PMCID: PMC7183816 DOI: 10.1007/s10096-020-03901-z 10.1007/s10096-020-03901-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.
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