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Wang X, Cheng L, Zhang D, Liu Z, Jiang L. Broad learning solution for rapid diagnosis of COVID-19. Biomed Signal Process Control 2023; 83:104724. [PMID: 36811035 PMCID: PMC9935280 DOI: 10.1016/j.bspc.2023.104724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 01/27/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023]
Abstract
COVID-19 has put all of humanity in a health dilemma as it spreads rapidly. For many infectious diseases, the delay of detection results leads to the spread of infection and an increase in healthcare costs. COVID-19 diagnostic methods rely on a large number of redundant labeled data and time-consuming data training processes to obtain satisfactory results. However, as a new epidemic, obtaining large clinical datasets is still challenging, which will inhibit the training of deep models. And a model that can really rapidly diagnose COVID-19 at all stages of the model has still not been proposed. To address these limitations, we combine feature attention and broad learning to propose a diagnostic system (FA-BLS) for COVID-19 pulmonary infection, which introduces a broad learning structure to address the slow diagnosis speed of existing deep learning methods. In our network, transfer learning is performed with ResNet50 convolutional modules with fixed weights to extract image features, and the attention mechanism is used to enhance feature representation. After that, feature nodes and enhancement nodes are generated by broad learning with random weights to adaptly select features for diagnosis. Finally, three publicly accessible datasets were used to evaluate our optimization model. It was determined that the FA-BLS model had a 26-130 times faster training speed than deep learning with a similar level of accuracy, which can achieve a fast and accurate diagnosis, achieve effective isolation from COVID-19 and the proposed method also opens up a new method for other types of chest CT image recognition problems.
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Affiliation(s)
- Xiaowei Wang
- School of Physical Science and Technology, Shenyang Normal University, Shenyang, 110034, China
| | - Liying Cheng
- School of Physical Science and Technology, Shenyang Normal University, Shenyang, 110034, China
| | - Dan Zhang
- Navigation College, Dalian Maritime University, Dalian, 116026, China
| | - Zuchen Liu
- School of Physical Science and Technology, Shenyang Normal University, Shenyang, 110034, China
| | - Longtao Jiang
- School of Physical Science and Technology, Shenyang Normal University, Shenyang, 110034, China
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Bhattacharjee V, Priya A, Kumari N, Anwar S. DeepCOVNet Model for COVID-19 Detection Using Chest X-Ray Images. WIRELESS PERSONAL COMMUNICATIONS 2023; 130:1399-1416. [PMID: 37168437 PMCID: PMC10088652 DOI: 10.1007/s11277-023-10336-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/25/2023] [Indexed: 05/13/2023]
Abstract
COVID-19 is an epidemic disease that has threatened all the people at worldwide scale and eventually became a pandemic It is a crucial task to differentiate COVID-19-affected patients from healthy patient populations. The need for technology enabled solutions is pertinent and this paper proposes a deep learning model for detection of COVID-19 using Chest X-Ray (CXR) images. In this research work, we provide insights on how to build robust deep learning based models for COVID-19 CXR image classification from Normal and Pneumonia affected CXR images. We contribute a methodical escort on preparation of data to produce a robust deep learning model. The paper prepared datasets by refactoring, using images from several datasets for ameliorate training of deep model. These recently published datasets enable us to build our own model and compare by using pre-trained models. The proposed experiments show the ability to work effectively to classify COVID-19 patients utilizing CXR. The empirical work, which uses a 3 convolutional layer based Deep Neural Network called "DeepCOVNet" to classify CXR images into 3 classes: COVID-19, Normal and Pneumonia cases, yielded an accuracy of 96.77% and a F1-score of 0.96 on two different combination of datasets.
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Affiliation(s)
| | - Ankita Priya
- Birla Institute of Technology Mesra, Ranchi, 835215 India
| | - Nandini Kumari
- Birla Institute of Technology Mesra, Ranchi, 835215 India
- Department of Data Science & Computer Application, Manipal Institute of Technology, Manipal, Manipal Academy of Higher Education, Manipal, 576104 Karnataka India
| | - Shamama Anwar
- Birla Institute of Technology Mesra, Ranchi, 835215 India
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Mavragani A, Wongsirichot T, Damkliang K, Navasakulpong A. Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation. JMIR Form Res 2023; 7:e42324. [PMID: 36780315 PMCID: PMC9976774 DOI: 10.2196/42324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 02/10/2023] [Accepted: 02/13/2023] [Indexed: 02/14/2023] Open
Abstract
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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Affiliation(s)
| | - Thakerng Wongsirichot
- Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand
| | - Kasikrit Damkliang
- Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand
| | - Asma Navasakulpong
- Division of Respiratory and Respiratory Critical Care Medicine, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand
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Kaya Y, Gürsoy E. A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection. Soft comput 2023; 27:5521-5535. [PMID: 36618761 PMCID: PMC9812349 DOI: 10.1007/s00500-022-07798-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/24/2022] [Indexed: 01/05/2023]
Abstract
COVID-19 is a virus that causes upper respiratory tract and lung infections. The number of cases and deaths increased daily during the pandemic. Once it is vital to diagnose such a disease in a timely manner, the researchers have focused on computer-aided diagnosis systems. Chest X-rays have helped monitor various lung diseases consisting COVID-19. In this study, we proposed a deep transfer learning approach with novel fine-tuning mechanisms to classify COVID-19 from chest X-ray images. We presented one classical and two new fine-tuning mechanisms to increase the model's performance. Two publicly available databases were combined and used for the study, which included 3616 COVID-19 and 1576 normal (healthy) and 4265 pneumonia X-ray images. The models achieved average accuracy rates of 95.62%, 96.10%, and 97.61%, respectively, for 3-class cases with fivefold cross-validation. Numerical results show that the third model reduced 81.92% of the total fine-tuning operations and achieved better results. The proposed approach is quite efficient compared with other state-of-the-art methods of detecting COVID-19.
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Affiliation(s)
- Yasin Kaya
- Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
| | - Ercan Gürsoy
- Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
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Green Financial Health Risk Early Monitoring of Commercial Banks Based on Neural Network Model in a Small Sample Environment. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2022; 2022:4613088. [PMID: 36193407 PMCID: PMC9526570 DOI: 10.1155/2022/4613088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 08/29/2022] [Accepted: 09/06/2022] [Indexed: 11/17/2022]
Abstract
Financial innovations emerge in an endless stream, and it is difficult for the regulatory measures and efforts of banks in various countries and the credit risk management level of commercial banks themselves to adapt to the increasingly complex risk environment faced by banks. In the process of building GFR (green financial risk) mixed governance model, the division of powers and responsibilities of governance subjects should be effectively defined. Therefore, it is very necessary to comprehensively and systematically study and grasp the characteristics, performance, and causes of commercial banks' GFR and build an early-warning model of commercial banks' GFR to comprehensively monitor the risks of banks, so as to reduce risks and avoid crises. Therefore, this paper uses the forward three-layer BPNN (BP neural network) technology to establish a real-time warning model of commercial banks' GFR. IL (input layer) to HL (hidden layer) adopts Sigmoid function, while HL to OL (output layer) function adopts linear function Purelin function. The results show that the test result of this method is greatly improved compared with the traditional method, and the correct rate is increased from 81.27% to 94.38%. It shows that the model in this paper has achieved a good warning effect of GFR for commercial banks.
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Seroprevalence of SARS-CoV-2 on health professionals via Bayesian estimation: a Brazilian case study before and after vaccines. Acta Trop 2022; 233:106551. [PMID: 35691330 PMCID: PMC9181309 DOI: 10.1016/j.actatropica.2022.106551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 06/01/2022] [Accepted: 06/03/2022] [Indexed: 11/20/2022]
Abstract
The increasing number of COVID-19 infections brought by the current pandemic has encouraged the scientific community to analyze the seroprevalence in populations to support health policies. In this context, accurate estimations of SARS-CoV-2 antibodies based on antibody tests metrics (e.g., specificity and sensitivity) and the study of population characteristics are essential. Here, we propose a Bayesian analysis using IgA and IgG antibody levels through multiple scenarios regarding data availability from different information sources to estimate the seroprevalence of health professionals in a Northeastern Brazilian city: no data available, data only related to the test performance, data from other regions. The study population comprises 432 subjects with more than 620 collections analyzed via IgA/IgG ELISA tests. We conducted the study in pre- and post-vaccination campaigns started in Brazil. We discuss the importance of aggregating available data from various sources to create informative prior knowledge. Considering prior information from the USA and Europe, the pre-vaccine seroprevalence means are 8.04% and 10.09% for IgG and 7.40% and 9.11% for IgA. For the post-vaccination campaign and considering local informative prior, the median is 84.83% for IgG, which confirms a sharp increase in the seroprevalence after vaccination. Additionally, stratification considering differences in sex, age (younger than 30 years, between 30 and 49 years, and older than 49 years), and presence of comorbidities are provided for all scenarios.
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Lins ID, Raupp LS, Maior CBS, de Barros Felipe FC, Moura MJDC, de Santana JMM, dos Santos A, Victor de Arruda Freitas M, Silva RN, Henrique da Conceição E, Ferraz JC, Araújo A, Fernandes M, Gomes AL. SerumCovid database: Description and preliminary analysis of serological COVID-19 diagnosis in healthcare workers. PLoS One 2022; 17:e0265016. [PMID: 35298515 PMCID: PMC8929608 DOI: 10.1371/journal.pone.0265016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 02/22/2022] [Indexed: 11/30/2022] Open
Abstract
Serological databases represent an important source of information to perceive COVID-19 impact on health professionals involved in combating the disease. This paper describes SerumCovid, a COVID-19 serological database focused on the diagnosis of health professionals, providing a preliminary analysis to contribute to the understanding of the antibody response to the SARS-CoV-2. The study population comprises 321 samples from 236 healthcare and frontline workers fighting COVID-19 in Vitória de Santo Antão, Brazil. Samples were collected from at least six days of symptoms to more than 100 days. The used immunoenzymatic assays were Euroimmun Anti-SARS-CoV-2 ELISA IgG and IgA. The most common gender in SerumCovid is female, while the most common age group is between 30 and 39 years old. However, no statistical differences were observed in either genders or age categories. The most reported symptoms were fatigue, headaches, and myalgia. Still, some subjects presented positive results for IgA after 130 days. Based on a temporal analysis, we have not identified general patterns as subjects presented high and low values of IgA and IgG with different evolution trends. Unexpectedly, for subjects with both serological tests, the outcome of IgA and IgG tests were the same (either positive or negative) for more than 80% of the samples. Therefore, SerumCovid helps better understand how COVID-19 affected healthcare and frontline workers, which increases knowledge about the infection and enables direct prevention actions.
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Affiliation(s)
- Isis Didier Lins
- CEERMA—Center for Risk Analysis, Reliability Engineering and Environmental Modeling, Universidade Federal de Pernambuco, Recife, Brazil
- Department of Production Engineering, Universidade Federal de Pernambuco, Recife, Brazil
| | - Leonardo Streck Raupp
- CEERMA—Center for Risk Analysis, Reliability Engineering and Environmental Modeling, Universidade Federal de Pernambuco, Recife, Brazil
- Department of Production Engineering, Universidade Federal de Pernambuco, Recife, Brazil
| | - Caio Bezerra Souto Maior
- CEERMA—Center for Risk Analysis, Reliability Engineering and Environmental Modeling, Universidade Federal de Pernambuco, Recife, Brazil
- Techology Center, Universidade Federal de Pernambuco, Caruaru, Brazil
- * E-mail:
| | - Felipe Cavalcanti de Barros Felipe
- CEERMA—Center for Risk Analysis, Reliability Engineering and Environmental Modeling, Universidade Federal de Pernambuco, Recife, Brazil
- Department of Production Engineering, Universidade Federal de Pernambuco, Recife, Brazil
| | - Márcio José das Chagas Moura
- CEERMA—Center for Risk Analysis, Reliability Engineering and Environmental Modeling, Universidade Federal de Pernambuco, Recife, Brazil
- Department of Production Engineering, Universidade Federal de Pernambuco, Recife, Brazil
| | - João Mateus Marques de Santana
- CEERMA—Center for Risk Analysis, Reliability Engineering and Environmental Modeling, Universidade Federal de Pernambuco, Recife, Brazil
- Department of Production Engineering, Universidade Federal de Pernambuco, Recife, Brazil
| | - Alexsandro dos Santos
- Vitória Academic Center, Universidade Federal de Pernambuco, Vitória de Santo Antão, Brazil
| | | | - Ramon Nascimento Silva
- Vitória Academic Center, Universidade Federal de Pernambuco, Vitória de Santo Antão, Brazil
| | | | - José Cândido Ferraz
- Vitória Academic Center, Universidade Federal de Pernambuco, Vitória de Santo Antão, Brazil
| | - Alice Araújo
- Vitória Academic Center, Universidade Federal de Pernambuco, Vitória de Santo Antão, Brazil
| | - Mariana Fernandes
- Vitória Academic Center, Universidade Federal de Pernambuco, Vitória de Santo Antão, Brazil
| | - Ana Lisa Gomes
- Vitória Academic Center, Universidade Federal de Pernambuco, Vitória de Santo Antão, Brazil
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Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Developing an artificial neural network for detecting COVID-19 disease. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2022; 11:2. [PMID: 35281397 PMCID: PMC8893090 DOI: 10.4103/jehp.jehp_387_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 04/29/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND From December 2019, atypical pneumonia termed COVID-19 has been increasing exponentially across the world. It poses a great threat and challenge to world health and the economy. Medical specialists face uncertainty in making decisions based on their judgment for COVID-19. Thus, this study aimed to establish an intelligent model based on artificial neural networks (ANNs) for diagnosing COVID-19. MATERIALS AND METHODS Using a single-center registry, we studied the records of 250 confirmed COVID-19 and 150 negative cases from February 9, 2020, to October 20, 2020. The correlation coefficient technique was used to determine the most significant variables of the ANN model. The variables at P < 0.05 were used for model construction. We applied the back-propagation technique for training a neural network on the dataset. After comparing different neural network configurations, the best configuration of ANN was acquired, then its strength has been evaluated. RESULTS After the feature selection process, a total of 18 variables were determined as the most relevant predictors for developing the ANN models. The results indicated that two nested loops' architecture of 9-10-15-2 (10 and 15 neurons used in layer 1 and layer 2, respectively) with the area under the curve of 0.982, the sensitivity of 96.4%, specificity of 90.6%, and accuracy of 94% was introduced as the best configuration model for COVID-19 diagnosis. CONCLUSION The proposed ANN-based clinical decision support system could be considered as a suitable computational technique for the frontline practitioner in early detection, effective intervention, and possibly a reduction of mortality in patients with COVID-19.
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Affiliation(s)
- Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Raoof Nopour
- Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
- Department of Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
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Gudigar A, Raghavendra U, Nayak S, Ooi CP, Chan WY, Gangavarapu MR, Dharmik C, Samanth J, Kadri NA, Hasikin K, Barua PD, Chakraborty S, Ciaccio EJ, Acharya UR. Role of Artificial Intelligence in COVID-19 Detection. SENSORS (BASEL, SWITZERLAND) 2021; 21:8045. [PMID: 34884045 PMCID: PMC8659534 DOI: 10.3390/s21238045] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 11/26/2021] [Accepted: 11/26/2021] [Indexed: 12/15/2022]
Abstract
The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Sneha Nayak
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia;
| | - Mokshagna Rohit Gangavarapu
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chinmay Dharmik
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia;
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Subrata Chakraborty
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
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Qaid TS, Mazaar H, Al-Shamri MYH, Alqahtani MS, Raweh AA, Alakwaa W. Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:9996737. [PMID: 34394338 PMCID: PMC8357494 DOI: 10.1155/2021/9996737] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 06/13/2021] [Accepted: 07/22/2021] [Indexed: 12/24/2022]
Abstract
The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world's healthcare systems at risk. The first step in stopping this outbreak is to detect the infection in its early stages, which will relieve the risk, control the outbreak's spread, and restore full functionality to the world's healthcare systems. Currently, PCR is the most prevalent diagnosis tool for COVID-19. However, chest X-ray images may play an essential role in detecting this disease, as they are successful for many other viral pneumonia diseases. Unfortunately, there are common features between COVID-19 and other viral pneumonia, and hence manual differentiation between them seems to be a critical problem and needs the aid of artificial intelligence. This research employs deep- and transfer-learning techniques to develop accurate, general, and robust models for detecting COVID-19. The developed models utilize either convolutional neural networks or transfer-learning models or hybridize them with powerful machine-learning techniques to exploit their full potential. For experimentation, we applied the proposed models to two data sets: the COVID-19 Radiography Database from Kaggle and a local data set from Asir Hospital, Abha, Saudi Arabia. The proposed models achieved promising results in detecting COVID-19 cases and discriminating them from normal and other viral pneumonia with excellent accuracy. The hybrid models extracted features from the flatten layer or the first hidden layer of the neural network and then fed these features into a classification algorithm. This approach enhanced the results further to full accuracy for binary COVID-19 classification and 97.8% for multiclass classification.
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Affiliation(s)
- Talal S. Qaid
- Computer Science Department, College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia
- Faculty of Computer Science and Engineering, Hodeidah University, Hodeidah, Yemen
| | - Hussein Mazaar
- Computer Science Department, College of Science and Arts in Tanumah, King Khalid University, Abha 61421, Saudi Arabia
| | - Mohammad Yahya H. Al-Shamri
- Computer Engineering Department, College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia
- Electrical Engineering Department, Faculty of Engineering, Ibb University, Ibb, Yemen
| | - Mohammed S. Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia
- BioImaging Unit, Space Research Centre, Department of Physics and Astronomy, University of Leicester, Leicester LE1 7RH, UK
| | - Abeer A. Raweh
- Computer Science Department, College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia
- Faculty of Computer Science and Engineering, Hodeidah University, Hodeidah, Yemen
| | - Wafaa Alakwaa
- Computer Science Department, College of Science and Arts in Tanumah, King Khalid University, Abha 61421, Saudi Arabia
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Bondugula RK, Udgata SK, Bommi NS. A Novel Weighted Consensus Machine Learning Model for COVID-19 Infection Classification Using CT Scan Images. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021; 48:1-12. [PMID: 34367873 PMCID: PMC8327899 DOI: 10.1007/s13369-021-05879-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 06/15/2021] [Indexed: 12/15/2022]
Abstract
As COVID-19 has spread rapidly, detection of the COVID-19 infection from radiology and radiography images is probably one of the quickest ways to diagnose the patients. Many researchers found the necessity to utilize chest X-ray and chest computed tomography imaging to diagnose COVID-19 infection. In this paper, our objective is to minimize the false negatives and false positives in the detection process. Reduction in the number of false negatives minimizes community spread of the COVID-19 pandemic. Reducing false positives help people avoid mental trauma and wasteful expenses. This paper proposes a novel weighted consensus model to minimize the number of false negatives and false positives without compromising accuracy. In the proposed novel weighted consensus model, the accuracy of individual classification models is normalized. While predicting, different models predict different classes, and the sum of the normalized accuracy for a particular class is then considered based on a predefined threshold value. We used traditional Machine Learning classification algorithms like Linear Regression, Support Vector Machine, k-Nearest Neighbours, Decision Tree, and Random Forest for the weighted consensus experimental evaluation. We predicted the classes, which provided better insights into the condition. The proposed model can perform as well as the existing state-of-the-art technique in terms of accuracy (99.64%) and reduce false negatives and false positives.
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Affiliation(s)
- Rohit Kumar Bondugula
- School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India
| | - Siba K. Udgata
- School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India
| | - Nitin Sai Bommi
- School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India
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