1851
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Zhang J, Xie Y, Pang G, Liao Z, Verjans J, Li W, Sun Z, He J, Li Y, Shen C, Xia Y. Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:879-890. [PMID: 33245693 PMCID: PMC8544953 DOI: 10.1109/tmi.2020.3040950] [Citation(s) in RCA: 98] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 11/10/2020] [Accepted: 11/22/2020] [Indexed: 05/24/2023]
Abstract
Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays can be of significant value for large-scale screening and epidemic prevention, particularly when other more sophisticated imaging modalities are not readily accessible. However, the emergence of novel mutated viruses causes a substantial dataset shift, which can greatly limit the performance of classification-based approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into a one-class classification-based anomaly detection problem. We therefore propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. If the anomaly score produced by the anomaly detection module is large enough, or the confidence score estimated by the confidence prediction module is small enough, the input will be accepted as an anomaly case (i.e., viral pneumonia). The major advantage of our approach over binary classification is that we avoid modeling individual viral pneumonia classes explicitly and treat all known viral pneumonia cases as anomalies to improve the one-class model. The proposed model outperforms binary classification models on the clinical X-VIRAL dataset that contains 5,977 viral pneumonia (no COVID-19) cases, 37,393 non-viral pneumonia or healthy cases. Moreover, when directly testing on the X-COVID dataset that contains 106 COVID-19 cases and 107 normal controls without any fine-tuning, our model achieves an AUC of 83.61% and sensitivity of 71.70%, which is comparable to the performance of radiologists reported in the literature.
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Affiliation(s)
- Jianpeng Zhang
- National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’an710072China
| | - Yutong Xie
- National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’an710072China
| | - Guansong Pang
- School of Computer ScienceThe University of AdelaideSA5005Australia
| | - Zhibin Liao
- School of Computer ScienceThe University of AdelaideSA5005Australia
| | - Johan Verjans
- School of Computer ScienceThe University of AdelaideSA5005Australia
| | | | - Zongji Sun
- National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’an710072China
| | - Jian He
- Department of RadiologyNanjing Drum Tower Hospital-Affiliated Hospital, Medical SchoolNanjing UniversityNanjing210029China
| | - Yi Li
- GreyBird Ventures, LLCConcordMA01742USA
| | - Chunhua Shen
- School of Computer ScienceThe University of AdelaideSA5005Australia
| | - Yong Xia
- National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’an710072China
- Research and Development Institute, Northwestern Polytechnical University in ShenzhenShenzhen518057China
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1852
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Kaur M, Kumar V, Yadav V, Singh D, Kumar N, Das NN. Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:8829829. [PMID: 33763196 PMCID: PMC7946481 DOI: 10.1155/2021/8829829] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 12/07/2020] [Accepted: 02/19/2021] [Indexed: 12/24/2022]
Abstract
COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models.
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Affiliation(s)
- Manjit Kaur
- Computer Science Engineering, School of Engineering and Applied Sciences, Bennett University, Greater Noida, 201310, India
| | - Vijay Kumar
- Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh, 177005, India
| | - Vaishali Yadav
- Department of Computer and Communication Engineering, School of Computing and Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India
| | - Dilbag Singh
- Computer Science Engineering, School of Engineering and Applied Sciences, Bennett University, Greater Noida, 201310, India
| | - Naresh Kumar
- Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, C-4 Block, Janakpuri, New Delhi 110058, India
| | - Nripendra Narayan Das
- Department of Information Technology, School of Computing and Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India
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1853
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Maior CBS, Santana JMM, Lins ID, Moura MJC. Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases. PLoS One 2021; 16:e0247839. [PMID: 33647062 PMCID: PMC7920391 DOI: 10.1371/journal.pone.0247839] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 02/13/2021] [Indexed: 01/08/2023] Open
Abstract
As SARS-CoV-2 has spread quickly throughout the world, the scientific community has spent major efforts on better understanding the characteristics of the virus and possible means to prevent, diagnose, and treat COVID-19. A valid approach presented in the literature is to develop an image-based method to support COVID-19 diagnosis using convolutional neural networks (CNN). Because the availability of radiological data is rather limited due to the novelty of COVID-19, several methodologies consider reduced datasets, which may be inadequate, biasing the model. Here, we performed an analysis combining six different databases using chest X-ray images from open datasets to distinguish images of infected patients while differentiating COVID-19 and pneumonia from 'no-findings' images. In addition, the performance of models created from fewer databases, which may imperceptibly overestimate their results, is discussed. Two CNN-based architectures were created to process images of different sizes (512 × 512, 768 × 768, 1024 × 1024, and 1536 × 1536). Our best model achieved a balanced accuracy (BA) of 87.7% in predicting one of the three classes ('no-findings', 'COVID-19', and 'pneumonia') and a specific balanced precision of 97.0% for 'COVID-19' class. We also provided binary classification with a precision of 91.0% for detection of sick patients (i.e., with COVID-19 or pneumonia) and 98.4% for COVID-19 detection (i.e., differentiating from 'no-findings' or 'pneumonia'). Indeed, despite we achieved an unrealistic 97.2% BA performance for one specific case, the proposed methodology of using multiple databases achieved better and less inflated results than from models with specific image datasets for training. Thus, this framework is promising for a low-cost, fast, and noninvasive means to support the diagnosis of COVID-19.
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Affiliation(s)
- Caio B. S. Maior
- 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 M. M. 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
| | - Isis D. 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
| | - Márcio J. C. 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
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1854
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Suri JS, Agarwal S, Gupta SK, Puvvula A, Biswas M, Saba L, Bit A, Tandel GS, Agarwal M, Patrick A, Faa G, Singh IM, Oberleitner R, Turk M, Chadha PS, Johri AM, Miguel Sanches J, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Ahluwalia P, Teji J, Al-Maini M, Dhanjil SK, Sockalingam M, Saxena A, Nicolaides A, Sharma A, Rathore V, Ajuluchukwu JNA, Fatemi M, Alizad A, Viswanathan V, Krishnan PK, Naidu S. A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence. Comput Biol Med 2021; 130:104210. [PMID: 33550068 PMCID: PMC7813499 DOI: 10.1016/j.compbiomed.2021.104210] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/03/2021] [Accepted: 01/03/2021] [Indexed: 02/06/2023]
Abstract
COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.
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Affiliation(s)
- Jasjit S Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA.
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA, USA; Department of Computer Science Engineering, PSIT, Kanpur, India
| | - Suneet K Gupta
- Department of Computer Science Engineering, Bennett University, India
| | - Anudeep Puvvula
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA; Annu's Hospitals for Skin and Diabetes, Nellore, AP, India
| | - Mainak Biswas
- Department of Computer Science Engineering, JIS University, Kolkata, India
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
| | - Arindam Bit
- Department of Biomedical Engineering, NIT, Raipur, India
| | - Gopal S Tandel
- Department of Computer Science Engineering, VNIT, Nagpur, India
| | - Mohit Agarwal
- Department of Computer Science Engineering, Bennett University, India
| | | | - Gavino Faa
- Department of Pathology - AOU of Cagliari, Italy
| | - Inder M Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA
| | | | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany
| | - Paramjit S Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Ontario, Canada
| | - J Miguel Sanches
- Institute of Systems and Robotics, Instituto Superior Tecnico, Lisboa, Portugal
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | | | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Rhode Island, USA
| | - David W Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | | | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Greece
| | - George Tsoulfas
- Aristoteleion University of Thessaloniki, Thessaloniki, Greece
| | | | | | - Vikas Agarwal
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK; Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, UK
| | - Puneet Ahluwalia
- Max Institute of Cancer Care, Max Superspeciality Hospital, New Delhi, India
| | - Jagjit Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, USA
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, Canada
| | | | | | - Ajit Saxena
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - Vijay Rathore
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA
| | | | - Mostafa Fatemi
- Dept. of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, MN, USA
| | - Azra Alizad
- Dept. of Radiology, Mayo Clinic College of Medicine and Science, MN, USA
| | - Vijay Viswanathan
- MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - P K Krishnan
- Neurology Department, Fortis Hospital, Bangalore, India
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN, USA
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1855
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Zheng F, Li L, Zhang X, Song Y, Huang Z, Chong Y, Chen Z, Zhu H, Wu J, Chen W, Lu Y, Yang Y, Zha Y, Zhao H, Shen J. Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning. Interdiscip Sci 2021; 13:273-285. [PMID: 33641077 PMCID: PMC7914048 DOI: 10.1007/s12539-021-00420-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 01/22/2021] [Accepted: 02/01/2021] [Indexed: 12/23/2022]
Abstract
Abstract Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic CT image diagnosis system to assist doctors in diagnosis. Previous studies devoted to COVID-19 in the past months focused mostly on discriminating COVID-19 infected patients from healthy persons and/or bacterial pneumonia patients, and have ignored typical viral pneumonia since it is hard to collect samples for viral pneumonia that is less frequent in adults. In addition, it is much more challenging to discriminate COVID-19 from typical viral pneumonia as COVID-19 is also a kind of virus. In this study, we have collected CT images of 262, 100, 219, and 78 persons for COVID-19, bacterial pneumonia, typical viral pneumonia, and healthy controls, respectively. To the best of our knowledge, this was the first study of quaternary classification to include also typical viral pneumonia. To effectively capture the subtle differences in CT images, we have constructed a new model by combining the ResNet50 backbone with SE blocks that was recently developed for fine image analysis. Our model was shown to outperform commonly used baseline models, achieving an overall accuracy of 0.94 with AUC of 0.96, recall of 0.94, precision of 0.95, and F1-score of 0.94. The model is available in https://github.com/Zhengfudan/COVID-19-Diagnosis-and-Pneumonia-Classification. Graphic Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1007/s12539-021-00420-z.
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Affiliation(s)
- Fudan Zheng
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Liang Li
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Xiang Zhang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510530, China
| | - Ying Song
- School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Ziwang Huang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Yutian Chong
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510220, China
| | - Zhiguang Chen
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.,National Supercomputing Center in Guangzhou, Guangzhou, 510006, China
| | - Huiling Zhu
- College of Information Science and Technology, Jinan University, Guangzhou, 510632, China
| | - Jiahao Wu
- School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Weifeng Chen
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Yutong Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.,National Supercomputing Center in Guangzhou, Guangzhou, 510006, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China. .,National Supercomputing Center in Guangzhou, Guangzhou, 510006, China.
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
| | - Huiying Zhao
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510530, China.
| | - Jun Shen
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510530, China.
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1856
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Dev K, Khowaja SA, Bist AS, Saini V, Bhatia S. Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks. Neural Comput Appl 2021; 35:1-16. [PMID: 33649695 PMCID: PMC7905772 DOI: 10.1007/s00521-020-05641-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 12/16/2020] [Indexed: 12/21/2022]
Abstract
The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for a potential alternative to reverse transcription-polymerase chain reaction due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis, but the unavailability of large-scale annotated data makes the clinical implementation of machine learning-based COVID detection difficult. Another issue is the usage of ImageNet pre-trained networks which does not extract reliable feature representations from medical images. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features. The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from well-known pre-trained networks to extract the features. The use of the convolution layer from COVIDNet ensures the extraction of representations relevant to the CXR modality. We also propose the use of ECOC for encoding multiclass problems to binary classification for improving the recognition performance. Experimental results show that HCN architecture is capable of achieving better results in comparison with the existing studies. The proposed method can accurately triage potential COVID-19 patients through CXR images for sharing the testing load and increasing the testing capacity.
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Affiliation(s)
- Kapal Dev
- CONNECT Centre, Trinity College Dublin, Dublin, Ireland
| | - Sunder Ali Khowaja
- Department of Telecommunication, Faculty of Engineering and Technology, University of Sindh, Jamshoro, Pakistan
| | | | | | - Surbhi Bhatia
- Department of Information Systems, King Faisal University, Hofuf, Saudi Arabia
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1857
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Mansourian M, Khademi S, Marateb HR. A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining. Diagnostics (Basel) 2021; 11:393. [PMID: 33669114 PMCID: PMC7996506 DOI: 10.3390/diagnostics11030393] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 02/13/2021] [Accepted: 02/17/2021] [Indexed: 02/07/2023] Open
Abstract
The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer's disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.
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Affiliation(s)
- Mahsa Mansourian
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran;
| | - Sadaf Khademi
- Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Isfahan 8174-67344, Iran;
| | - Hamid Reza Marateb
- Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Isfahan 8174-67344, Iran;
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1858
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Osman AH, Aljahdali HM, Altarrazi SM, Ahmed A. SOM-LWL method for identification of COVID-19 on chest X-rays. PLoS One 2021; 16:e0247176. [PMID: 33626053 PMCID: PMC7904146 DOI: 10.1371/journal.pone.0247176] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 02/02/2021] [Indexed: 12/21/2022] Open
Abstract
The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures.
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Affiliation(s)
- Ahmed Hamza Osman
- Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, Saudi Arabia
| | - Hani Moetque Aljahdali
- Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, Saudi Arabia
| | - Sultan Menwer Altarrazi
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, Saudi Arabia
| | - Ali Ahmed
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, Saudi Arabia
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1859
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Pathan S, Siddalingaswamy PC, Ali T. Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture. Appl Soft Comput 2021; 104:107238. [PMID: 33649705 PMCID: PMC7903912 DOI: 10.1016/j.asoc.2021.107238] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 02/21/2021] [Accepted: 02/21/2021] [Indexed: 12/13/2022]
Abstract
The novel coronavirus termed as covid-19 has taken the world by its crutches affecting innumerable lives with devastating impact on the global economy and public health. One of the major ways to control the spread of this disease is identification in the initial stage, so that isolation and treatment could be initiated. Due to the lack of automated auxiliary diagnostic medical tools, availability of lesser sensitivity testing kits, and limited availability of healthcare professionals, the pandemic has spread like wildfire across the world. Certain recent findings state that chest X-ray scans contain salient information regarding the onset of the virus, the information can be analyzed so that the diagnosis and treatment can be initiated at an earlier stage. This is where artificial intelligence meets the diagnostic capabilities of experienced clinicians. The objective of the proposed research is to contribute towards fighting the global pandemic by developing an automated image analysis module for identifying covid-19 affected chest X-ray scans by employing an optimized Convolution Neural Network (CNN) model. The aforementioned objective is achieved in the following manner by developing three classification models, (i) ensemble of ResNet 50-Error Correcting Output Code (ECOC) model, (ii) CNN optimized using Grey Wolf Optimizer (GWO) and, (iii) CNN optimized using Whale Optimization + BAT algorithm. The novelty of the proposed method lies in the automatic tuning of hyper parameters considering a hierarchy of MultiLayer Perceptron (MLP), feature extraction, and optimization ensemble. A 100% classification accuracy was obtained in classifying covid-19 images. Classification accuracy of 98.8% and 96% were obtained for dataset 1 and dataset 2 respectively for classification into covid-19, normal, and viral pneumonia cases. The proposed method can be adopted in a clinical setting for assisting radiologists and it can also be employed in remote areas to facilitate the faster screening of affected patients.
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Affiliation(s)
- Sameena Pathan
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - P C Siddalingaswamy
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Tanweer Ali
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
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1860
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Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification. SENSORS 2021; 21:s21041511. [PMID: 33671583 PMCID: PMC7926887 DOI: 10.3390/s21041511] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 02/15/2021] [Accepted: 02/16/2021] [Indexed: 01/01/2023]
Abstract
The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals. The method combines multirate processing, wavelet decomposition and frequency content-based subband coefficient selection and machine learning techniques. Multirate processing and features selection is used to reduce the amount of information processed thus reducing the computational complexity of the proposed system relative to the equivalent fixed-rate solutions. Frequency content-dependent subband coefficient selection enhances the compression gain and reduces the transmission activity and computational cost of the post cloud-based classification. We have used MIT-BIH dataset for our experiments. To avoid overfitting and biasness, the performance of considered classifiers is studied by using five-fold cross validation (5CV) and a novel proposed partial blind protocol. The designed method achieves more than 12-fold computational gain while assuring an appropriate signal reconstruction. The compression gain is 13 times compared to fixed-rate counterparts and the highest classification accuracies are 97.06% and 92.08% for the 5CV and partial blind cases, respectively. Results suggest the feasibility of detecting cardiac arrhythmias using the proposed approach.
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1861
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COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning. SENSORS 2021; 21:s21041480. [PMID: 33672585 PMCID: PMC8078171 DOI: 10.3390/s21041480] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 02/15/2021] [Accepted: 02/17/2021] [Indexed: 12/15/2022]
Abstract
Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly around the world, thought to be originated from Wuhan in China and is responsible for a large number of deaths. Earlier detection of the COVID-19 through accurate diagnosis, particularly for the cases with no obvious symptoms, may decrease the patient's death rate. Chest X-ray images are primarily used for the diagnosis of this disease. This research has proposed a machine vision approach to detect COVID-19 from the chest X-ray images. The features extracted by the histogram-oriented gradient (HOG) and convolutional neural network (CNN) from X-ray images were fused to develop the classification model through training by CNN (VGGNet). Modified anisotropic diffusion filtering (MADF) technique was employed for better edge preservation and reduced noise from the images. A watershed segmentation algorithm was used in order to mark the significant fracture region in the input X-ray images. The testing stage considered generalized data for performance evaluation of the model. Cross-validation analysis revealed that a 5-fold strategy could successfully impair the overfitting problem. This proposed feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%. When compared to other classification techniques, such as ANN, KNN, and SVM, the CNN technique used in this study showed better classification performance. K-fold cross-validation demonstrated that the proposed feature fusion technique (98.36%) provided higher accuracy than the individual feature extraction methods, such as HOG (87.34%) or CNN (93.64%).
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1862
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El Asnaoui K. Design ensemble deep learning model for pneumonia disease classification. INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL 2021; 10:55-68. [PMID: 33643764 PMCID: PMC7896551 DOI: 10.1007/s13735-021-00204-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 01/06/2021] [Accepted: 01/19/2021] [Indexed: 05/23/2023]
Abstract
With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic of covid-19 that is kind of pneumonia. In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification. The ensembles used are mainly based on fined-tuned versions of (InceptionResNet_V2, ResNet50 and MobileNet_V2). We collected a new dataset containing 6087 chest X-ray images in which we conduct comprehensive experiments. As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score. In addition, ensemble of 3 models (ResNet50 with MobileNet_V2 with InceptionResNet_V2) shows more accurate than other ensembles constructed (94.84% of F1 score).
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Affiliation(s)
- Khalid El Asnaoui
- National School of Applied Sciences (ENSA), Department of Computer Sciences, Mohammed First University, BP: 669, 60000 Oujda, Morocco
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1863
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Females and Males Show Differences in Early-Stage Transcriptomic Biomarkers of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma. Diagnostics (Basel) 2021; 11:diagnostics11020347. [PMID: 33669819 PMCID: PMC7922551 DOI: 10.3390/diagnostics11020347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 02/15/2021] [Accepted: 02/17/2021] [Indexed: 12/25/2022] Open
Abstract
The incidence and mortality rates of lung cancers are different between females and males. Therefore, sex information should be an important part of how to train and optimize a diagnostic model. However, most of the existing studies do not fully utilize this information. This study carried out a comparative investigation between sex-specific models and sex-independent models. Three feature selection algorithms and five classifiers were utilized to evaluate the contribution of the sex information to the detection of early-stage lung cancers. Both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) showed that the sex-specific models outperformed the sex-independent detection of early-stage lung cancers. The Venn plots suggested that females and males shared only a few transcriptomic biomarkers of early-stage lung cancers. Our experimental data suggested that sex information should be included in optimizing disease diagnosis models.
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1864
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Elzeki OM, Shams M, Sarhan S, Abd Elfattah M, Hassanien AE. COVID-19: a new deep learning computer-aided model for classification. PeerJ Comput Sci 2021; 7:e358. [PMID: 33817008 PMCID: PMC7959596 DOI: 10.7717/peerj-cs.358] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 12/19/2020] [Indexed: 05/09/2023]
Abstract
Chest X-ray (CXR) imaging is one of the most feasible diagnosis modalities for early detection of the infection of COVID-19 viruses, which is classified as a pandemic according to the World Health Organization (WHO) report in December 2019. COVID-19 is a rapid natural mutual virus that belongs to the coronavirus family. CXR scans are one of the vital tools to early detect COVID-19 to monitor further and control its virus spread. Classification of COVID-19 aims to detect whether a subject is infected or not. In this article, a model is proposed for analyzing and evaluating grayscale CXR images called Chest X-Ray COVID Network (CXRVN) based on three different COVID-19 X-Ray datasets. The proposed CXRVN model is a lightweight architecture that depends on a single fully connected layer representing the essential features and thus reducing the total memory usage and processing time verse pre-trained models and others. The CXRVN adopts two optimizers: mini-batch gradient descent and Adam optimizer, and the model has almost the same performance. Besides, CXRVN accepts CXR images in grayscale that are a perfect image representation for CXR and consume less memory storage and processing time. Hence, CXRVN can analyze the CXR image with high accuracy in a few milliseconds. The consequences of the learning process focus on decision making using a scoring function called SoftMax that leads to high rate true-positive classification. The CXRVN model is trained using three different datasets and compared to the pre-trained models: GoogleNet, ResNet and AlexNet, using the fine-tuning and transfer learning technologies for the evaluation process. To verify the effectiveness of the CXRVN model, it was evaluated in terms of the well-known performance measures such as precision, sensitivity, F1-score and accuracy. The evaluation results based on sensitivity, precision, recall, accuracy, and F1 score demonstrated that, after GAN augmentation, the accuracy reached 96.7% in experiment 2 (Dataset-2) for two classes and 93.07% in experiment-3 (Dataset-3) for three classes, while the average accuracy of the proposed CXRVN model is 94.5%.
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Affiliation(s)
- Omar M. Elzeki
- Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Mahmoud Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt
| | - Shahenda Sarhan
- Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | | | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Egypt, Cairo, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
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1865
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Rana S, Rosenfeld AB. Investigating volumetric repainting to mitigate interplay effect on 4D robustly optimized lung cancer plans in pencil beam scanning proton therapy. J Appl Clin Med Phys 2021; 22:107-118. [PMID: 33599391 PMCID: PMC7984493 DOI: 10.1002/acm2.13183] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/19/2021] [Accepted: 01/05/2021] [Indexed: 12/16/2022] Open
Abstract
Purpose The interplay effect between dynamic pencil proton beams and motion of the lung tumor presents a challenge in treating lung cancer patients in pencil beam scanning (PBS) proton therapy. The main purpose of the current study was to investigate the interplay effect on the volumetric repainting lung plans with beam delivery in alternating order (“down” and “up” directions), and explore the number of volumetric repaintings needed to achieve acceptable lung cancer PBS proton plan. Method The current retrospective study included ten lung cancer patients. The total dose prescription to the clinical target volume (CTV) was 70 Gy(RBE) with a fractional dose of 2 Gy(RBE). All treatment plans were robustly optimized on all ten phases in the 4DCT data set. The Monte Carlo algorithm was used for the 4D robust optimization, as well as for the final dose calculation. The interplay effect was evaluated for both the nominal (i.e., without repainting) as well as volumetric repainting plans. The interplay evaluation was carried out for each of the ten different phases as the starting phases. Several dosimetric metrics were included to evaluate the worst‐case scenario (WCS) and bandwidth based on the results obtained from treatment delivery starting in ten different breathing phases. Results The number of repaintings needed to meet the criteria 1 (CR1) of target coverage (D95% ≥ 98% and D99% ≥ 97%) ranged from 2 to 10. The number of repaintings needed to meet the CR1 of maximum dose (ΔD1% < 1.5%) ranged from 2 to 7. Similarly, the number of repaintings needed to meet CR1 of homogeneity index (ΔHI < 0.03) ranged from 3 to 10. For the target coverage region, the number of repaintings needed to meet CR1 of bandwidth (<100 cGy) ranged from 3 to 10, whereas for the high‐dose region, the number of repaintings needed to meet CR1 of bandwidth (<100 cGy) ranged from 1 to 7. Based on the overall plan evaluation criteria proposed in the current study, acceptable plans were achieved for nine patients, whereas one patient had acceptable plan with a minor deviation. Conclusion The number of repaintings required to mitigate the interplay effect in PBS lung cancer (tumor motion < 15 mm) was found to be highly patient dependent. For the volumetric repainting with an alternating order, a patient‐specific interplay evaluation strategy must be adopted. Determining the optimal number of repaintings based on the bandwidth and WCS approach could mitigate the interplay effect in PBS lung cancer treatment.
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Affiliation(s)
- Suresh Rana
- Department of Medical PhysicsThe Oklahoma Proton CenterOklahoma CityOklahomaUSA
- Department of Radiation OncologyMiami Cancer InstituteBaptist Health South FloridaMiamiFLUSA
- Department of Radiation OncologyHerbert Wertheim College of MedicineFlorida International UniversityMiamiFLUSA
- Centre for Medical Radiation Physics (CMRP)University of WollongongWollongongNSWAustralia
| | - Anatoly B. Rosenfeld
- Centre for Medical Radiation Physics (CMRP)University of WollongongWollongongNSWAustralia
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1866
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Javaheri T, Homayounfar M, Amoozgar Z, Reiazi R, Homayounieh F, Abbas E, Laali A, Radmard AR, Gharib MH, Mousavi SAJ, Ghaemi O, Babaei R, Mobin HK, Hosseinzadeh M, Jahanban-Esfahlan R, Seidi K, Kalra MK, Zhang G, Chitkushev LT, Haibe-Kains B, Malekzadeh R, Rawassizadeh R. CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images. NPJ Digit Med 2021; 4:29. [PMID: 33603193 PMCID: PMC7893172 DOI: 10.1038/s41746-021-00399-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 12/10/2020] [Indexed: 12/21/2022] Open
Abstract
Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.
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Affiliation(s)
- Tahereh Javaheri
- Health Informatics Lab, Metropolitan College, Boston University, Boston, USA
| | - Morteza Homayounfar
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Zohreh Amoozgar
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Reza Reiazi
- Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Medical Physics, School of Medicine, Iran university of Medical Sciences, Tehran, Iran
| | - Fatemeh Homayounieh
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Engy Abbas
- Joint Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Azadeh Laali
- Department of Infectious Diseases, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Amir Reza Radmard
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Hadi Gharib
- Department of Radiology and Golestan Rheumatology Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | | | - Omid Ghaemi
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Rosa Babaei
- Department of Radiology, Iran University of Medical Sciences, Tehran, Iran
| | - Hadi Karimi Mobin
- Department of Radiology, Iran University of Medical Sciences, Tehran, Iran
| | - Mehdi Hosseinzadeh
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Rana Jahanban-Esfahlan
- Department of Medical Biotechnology, School of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Khaled Seidi
- Department of Medical Biotechnology, School of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Guanglan Zhang
- Health Informatics Lab, Metropolitan College, Boston University, Boston, USA
- Department of Computer Science, Metropolitan College, Boston University, Boston, USA
| | - L T Chitkushev
- Health Informatics Lab, Metropolitan College, Boston University, Boston, USA
- Department of Computer Science, Metropolitan College, Boston University, Boston, USA
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Reza Malekzadeh
- Digestive Disease Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Reza Rawassizadeh
- Health Informatics Lab, Metropolitan College, Boston University, Boston, USA.
- Department of Computer Science, Metropolitan College, Boston University, Boston, USA.
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1867
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Nawaz MS, Fournier-Viger P, Shojaee A, Fujita H. Using artificial intelligence techniques for COVID-19 genome analysis. APPL INTELL 2021; 51:3086-3103. [PMID: 34764587 PMCID: PMC7888282 DOI: 10.1007/s10489-021-02193-w] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/04/2021] [Indexed: 12/25/2022]
Abstract
The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province, China. COVID-19 genome sequencing is critical to understanding the virus behavior, its origin, how fast it mutates, and for the development of drugs/vaccines and effective preventive strategies. This paper investigates the use of artificial intelligence techniques to learn interesting information from COVID-19 genome sequences. Sequential pattern mining (SPM) is first applied on a computer-understandable corpus of COVID-19 genome sequences to see if interesting hidden patterns can be found, which reveal frequent patterns of nucleotide bases and their relationships with each other. Second, sequence prediction models are applied to the corpus to evaluate if nucleotide base(s) can be predicted from previous ones. Third, for mutation analysis in genome sequences, an algorithm is designed to find the locations in the genome sequences where the nucleotide bases are changed and to calculate the mutation rate. Obtained results suggest that SPM and mutation analysis techniques can reveal interesting information and patterns in COVID-19 genome sequences to examine the evolution and variations in COVID-19 strains respectively.
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Affiliation(s)
- M. Saqib Nawaz
- School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Philippe Fournier-Viger
- School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | | | - Hamido Fujita
- Faculty of Software and Information Science, Iwate Prefectural University, Iwate, Japan
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1868
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Charles PH, Crowe S, Kairn T. Recommendations for simulating and measuring with biofabricated lung equivalent materials based on atomic composition analysis. Phys Eng Sci Med 2021; 44:331-335. [PMID: 33591538 DOI: 10.1007/s13246-021-00979-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/28/2021] [Indexed: 11/25/2022]
Abstract
Monte Carlo simulations of lung equivalent materials often involve the density being artificially lowered rather than a true lung tissue (or equivalent plastic) and air composition being simulated. This study used atomic composition analysis to test the suitability of this method. Atomic composition analysis was also used to test the suitability of 3D printing PLA or ABS with air to simulate lung tissue. It was found that there was minimal atomic composition difference when using an artificially lowered density, with a 0.8 % difference in Nitrogen the largest observed. Therefore, excluding infill pattern effects, lowering the density of the lung tissue (or plastic) in simulations should be sufficiently accurate to simulate an inhaled lung, without the need to explicitly include the air component. The average electron density of 3D printed PLA and air, and ABS and air were just 0.3 % and 1.3 % different to inhaled lung, confirming their adequacy for MV photon dosimetry. However large average atomic number differences (5.6 % and 20.4 % respectively) mean that they are unlikely to be suitable for kV photon dosimetry.
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Affiliation(s)
- Paul H Charles
- Herston Biofabrication Institute, Brisbane, QLD, Australia. .,School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia. .,Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD, Australia.
| | - Scott Crowe
- Herston Biofabrication Institute, Brisbane, QLD, Australia.,School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia.,Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD, Australia.,Cancer Care Services, Royal Brisbane & Women's Hospital, Brisbane, QLD, Australia
| | - Tanya Kairn
- Herston Biofabrication Institute, Brisbane, QLD, Australia.,School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia.,Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD, Australia.,Cancer Care Services, Royal Brisbane & Women's Hospital, Brisbane, QLD, Australia
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1869
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Towards zero radiation isocentre size: minimising radiation beam isocentricity on Elekta linear accelerators by means of optimising look-up tables. Phys Eng Sci Med 2021; 44:557-563. [PMID: 33591539 DOI: 10.1007/s13246-021-00981-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 02/04/2021] [Indexed: 10/22/2022]
Abstract
The most important geometric characteristics of SRS/SBRT treatments are precise target localisation and precise aiming of the radiation beam at the target. The AAPM-RSS Medical Physics Practice Guideline 9.a. for SRS/SBRT recommends that the radiation isocentricity (i.e. beam deviation from the isocentre) should not exceed 1 mm for SRS and 1.5 mm for SBRT. Minimising the beam deviations from the treatment target, largely due to the gantry sag, can improve the accuracy of radiosurgery and stereotactic treatments and commonly beam steering parameters are optimised to achieve this objective. This study aims to investigate, as a proof of concept, if it is possible to eliminate the beam deviations on Elekta linear accelerators altogether by optimising gantry angle dependent beam steering parameters, as stored in look-up tables. The investigation used the EPID-based Winston-Lutz test at 13 gantry angles separated every 30° (from - 180° to + 180°). Elekta linacs have two look-up tables that can be customised explicitly for radial beam angle and transverse beam position. Modifications of the radial look-up table were limited by the radial beam asymmetry inhibit of more than 5%, as measured by the linac in-built ionisation chamber. Therefore, only small radial beam deviation reductions of 0.1 mm were achieved (on average from 0.37 to 0.26 mm) while radial beam symmetry changed significantly by up to ± 7%, depending on the gantry angle as measured by the IC Profiler™. The optimised transverse look-up table resulted in reduction of transverse beam deviations to almost zero (on average from 0.20 to 0.03 mm), however, that changed the transverse beam symmetry by almost a constant value of 1%, as measured by the IC Profiler™. Ideally, two additional look-up tables are needed for effective beam steering, one for radial beam position and one for transverse beam angle. Four look-up tables in total would enable customising beam centre position and beam symmetry at any gantry angle that would minimize radiation isocentre size without compromising beam symmetry.
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1870
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Chattopadhyay S, Dey A, Singh PK, Geem ZW, Sarkar R. COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer. Diagnostics (Basel) 2021; 11:diagnostics11020315. [PMID: 33671992 PMCID: PMC7919377 DOI: 10.3390/diagnostics11020315] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/28/2021] [Accepted: 02/09/2021] [Indexed: 12/11/2022] Open
Abstract
The COVID-19 virus is spreading across the world very rapidly. The World Health Organization (WHO) declared it a global pandemic on 11 March 2020. Early detection of this virus is necessary because of the unavailability of any specific drug. The researchers have developed different techniques for COVID-19 detection, but only a few of them have achieved satisfactory results. There are three ways for COVID-19 detection to date, those are real-time reverse transcription-polymerize chain reaction (RT-PCR), Computed Tomography (CT), and X-ray plays. In this work, we have proposed a less expensive computational model for automatic COVID-19 detection from Chest X-ray and CT-scan images. Our paper has a two-fold contribution. Initially, we have extracted deep features from the image dataset and then introduced a completely novel meta-heuristic feature selection approach, named Clustering-based Golden Ratio Optimizer (CGRO). The model has been implemented on three publicly available datasets, namely the COVID CT-dataset, SARS-Cov-2 dataset, and Chest X-Ray dataset, and attained state-of-the-art accuracies of 99.31%, 98.65%, and 99.44%, respectively.
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Affiliation(s)
- Soham Chattopadhyay
- Department of Electrical Engineering, Jadavpur University, Kolkata 700032, India;
| | - Arijit Dey
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Simhat, Haringhata, Nadia 741249, India;
| | - Pawan Kumar Singh
- Department of Information Technology, Jadavpur University, Kolkata 700106, India;
| | - Zong Woo Geem
- College of IT Convergence, Gachon University, 1342 Seongnam Daero, Seongnam 13120, Korea
- Correspondence:
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India;
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1871
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Robinson C, Trivedi A, Blazes M, Ortiz A, Desbiens J, Gupta S, Dodhia R, Bhatraju PK, Liles WC, Lee A, Kalpathy-Cramer J, Ferres JML. Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.02.11.20196766. [PMID: 33594382 PMCID: PMC7885941 DOI: 10.1101/2021.02.11.20196766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics -- a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process, forcing the models to identify pulmonary features from the images while penalizing them for learning features that can discriminate between the original datasets that the images come from. We find that models trained in this way indeed have better generalization performance on unseen data; in the best case we found that it improved AUC by 0.13 on held out data. We further find that this outperforms masking out non-lung parts of the CXRs and performing histogram equalization, both of which are recently proposed methods for removing biases in CXR datasets.
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Affiliation(s)
| | | | | | | | | | | | | | - Pavan K. Bhatraju
- Department of Medicine and Sepsis Center of Research Excellence, University of Washington (SCORE-UW)
| | - W. Conrad Liles
- Department of Medicine and Sepsis Center of Research Excellence, University of Washington (SCORE-UW)
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1872
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Xi D, Jiang W, Shao Y, Song X, Chen Y, Liu M, Gu W, Li Q. Retrospective analysis of the bleeding risk induced by oral antiplatelet drugs during radiotherapy. Medicine (Baltimore) 2021; 100:e24580. [PMID: 33578556 PMCID: PMC7886460 DOI: 10.1097/md.0000000000024580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 01/14/2021] [Indexed: 01/05/2023] Open
Abstract
We conducted this retrospective analysis to assess whether oral antiplatelet drugs (APDs) during radiotherapy increase bleeding risk.Patients who underwent radiotherapy for esophageal cancer (EC) in the Third Affiliated Hospital of Soochow University from January 2015 to December 2019 were screened. After the differences in clinical parameters were eliminated by a propensity-score matched (PSM) analysis at a 1:1 ratio, the thrombocytopenia, consumption of platelet-increasing drugs, suspension of radiotherapy, and bleeding in patients taking APDs were compared with those in the control group.A total of 986 patients were included in the original dataset. Of these, 34 patients took APDs during radiotherapy. After matching, the APD and control groups each retained 31 patients. There was no significant difference in platelet concentrations between the two groups before radiotherapy (P = .524). The lowest platelet concentration during radiotherapy in the APD group was significantly lower (P = .033). The consumption of platelet-increasing drugs in the APD group was higher than that in the control group (P < .05). However, there was no significant difference in the average number of days of radiotherapy suspension because of thrombocytopenia (P = .933) and no significant difference in the incidence of bleeding between the two groups (P = .605).Oral APDs during radiotherapy lead to a further decrease in platelet concentration, but timely and adequate application of platelet-increasing drugs can avoid the increased risk of bleeding and the reduced efficacy of radiotherapy.
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1873
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Albahli S, Yar GNAH. Fast and Accurate Detection of COVID-19 Along With 14 Other Chest Pathologies Using a Multi-Level Classification: Algorithm Development and Validation Study. J Med Internet Res 2021; 23:e23693. [PMID: 33529154 PMCID: PMC7879720 DOI: 10.2196/23693] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 10/12/2020] [Accepted: 01/31/2021] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND COVID-19 has spread very rapidly, and it is important to build a system that can detect it in order to help an overwhelmed health care system. Many research studies on chest diseases rely on the strengths of deep learning techniques. Although some of these studies used state-of-the-art techniques and were able to deliver promising results, these techniques are not very useful if they can detect only one type of disease without detecting the others. OBJECTIVE The main objective of this study was to achieve a fast and more accurate diagnosis of COVID-19. This study proposes a diagnostic technique that classifies COVID-19 x-ray images from normal x-ray images and those specific to 14 other chest diseases. METHODS In this paper, we propose a novel, multilevel pipeline, based on deep learning models, to detect COVID-19 along with other chest diseases based on x-ray images. This pipeline reduces the burden of a single network to classify a large number of classes. The deep learning models used in this study were pretrained on the ImageNet dataset, and transfer learning was used for fast training. The lungs and heart were segmented from the whole x-ray images and passed onto the first classifier that checks whether the x-ray is normal, COVID-19 affected, or characteristic of another chest disease. If it is neither a COVID-19 x-ray image nor a normal one, then the second classifier comes into action and classifies the image as one of the other 14 diseases. RESULTS We show how our model uses state-of-the-art deep neural networks to achieve classification accuracy for COVID-19 along with 14 other chest diseases and normal cases based on x-ray images, which is competitive with currently used state-of-the-art models. Due to the lack of data in some classes such as COVID-19, we applied 10-fold cross-validation through the ResNet50 model. Our classification technique thus achieved an average training accuracy of 96.04% and test accuracy of 92.52% for the first level of classification (ie, 3 classes). For the second level of classification (ie, 14 classes), our technique achieved a maximum training accuracy of 88.52% and test accuracy of 66.634% by using ResNet50. We also found that when all the 16 classes were classified at once, the overall accuracy for COVID-19 detection decreased, which in the case of ResNet50 was 88.92% for training data and 71.905% for test data. CONCLUSIONS Our proposed pipeline can detect COVID-19 with a higher accuracy along with detecting 14 other chest diseases based on x-ray images. This is achieved by dividing the classification task into multiple steps rather than classifying them collectively.
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Affiliation(s)
- Saleh Albahli
- Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
- Department of Computer Science, Kent State University, Kent, OH, United States
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1874
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Castillo O, Melin P. A Novel Method for a COVID-19 Classification of Countries Based on an Intelligent Fuzzy Fractal Approach. Healthcare (Basel) 2021; 9:healthcare9020196. [PMID: 33578902 PMCID: PMC7916684 DOI: 10.3390/healthcare9020196] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/06/2021] [Accepted: 02/07/2021] [Indexed: 12/22/2022] Open
Abstract
We outline in this article a hybrid intelligent fuzzy fractal approach for classification of countries based on a mixture of fractal theoretical concepts and fuzzy logic mathematical constructs. The mathematical definition of the fractal dimension provides a way to estimate the complexity of the non-linear dynamic behavior exhibited by the time series of the countries. Fuzzy logic offers a way to represent and handle the inherent uncertainty of the classification problem. The hybrid intelligent approach is composed of a fuzzy system formed by a set of fuzzy rules that uses the fractal dimensions of the data as inputs and produce as a final output the classification of countries. The hybrid approach calculations are based on the COVID-19 data of confirmed and death cases. The main contribution is the proposed hybrid approach composed of the fractal dimension definition and fuzzy logic concepts for achieving an accurate classification of countries based on the complexity of the COVID-19 time series data. Publicly available datasets of 11 countries have been the basis to construct the fuzzy system and 15 different countries were considered in the validation of the proposed classification approach. Simulation results show that a classification accuracy over 93% can be achieved, which can be considered good for this complex problem.
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1875
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Elzeki OM, Abd Elfattah M, Salem H, Hassanien AE, Shams M. A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset. PeerJ Comput Sci 2021; 7:e364. [PMID: 33817014 PMCID: PMC7959632 DOI: 10.7717/peerj-cs.364] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 12/30/2020] [Indexed: 05/31/2023]
Abstract
BACKGROUND AND PURPOSE COVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to people's health. Experimental medical tests and analysis have shown that the infection of lungs occurs in almost all COVID-19 patients. Although Computed Tomography of the chest is a useful imaging method for diagnosing diseases related to the lung, chest X-ray (CXR) is more widely available, mainly due to its lower price and results. Deep learning (DL), one of the significant popular artificial intelligence techniques, is an effective way to help doctors analyze how a large number of CXR images is crucial to performance. MATERIALS AND METHODS In this article, we propose a novel perceptual two-layer image fusion using DL to obtain more informative CXR images for a COVID-19 dataset. To assess the proposed algorithm performance, the dataset used for this work includes 87 CXR images acquired from 25 cases, all of which were confirmed with COVID-19. The dataset preprocessing is needed to facilitate the role of convolutional neural networks (CNN). Thus, hybrid decomposition and fusion of Nonsubsampled Contourlet Transform (NSCT) and CNN_VGG19 as feature extractor was used. RESULTS Our experimental results show that imbalanced COVID-19 datasets can be reliably generated by the algorithm established here. Compared to the COVID-19 dataset used, the fuzed images have more features and characteristics. In evaluation performance measures, six metrics are applied, such as QAB/F, QMI, PSNR, SSIM, SF, and STD, to determine the evaluation of various medical image fusion (MIF). In the QMI, PSNR, SSIM, the proposed algorithm NSCT + CNN_VGG19 achieves the greatest and the features characteristics found in the fuzed image is the largest. We can deduce that the proposed fusion algorithm is efficient enough to generate CXR COVID-19 images that are more useful for the examiner to explore patient status. CONCLUSIONS A novel image fusion algorithm using DL for an imbalanced COVID-19 dataset is the crucial contribution of this work. Extensive results of the experiment display that the proposed algorithm NSCT + CNN_VGG19 outperforms competitive image fusion algorithms.
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Affiliation(s)
- Omar M. Elzeki
- Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt
| | | | - Hanaa Salem
- Communications and Computers Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Gamasa, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
| | - Mahmoud Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt
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1876
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Islam MM, Karray F, Alhajj R, Zeng J. A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19). IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:30551-30572. [PMID: 34976571 PMCID: PMC8675557 DOI: 10.1109/access.2021.3058537] [Citation(s) in RCA: 114] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 02/06/2021] [Indexed: 05/03/2023]
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. The aim of this paper is to facilitate experts (medical or otherwise) and technicians in understanding the ways deep learning techniques are used in this regard and how they can be potentially further utilized to combat the outbreak of COVID-19.
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Affiliation(s)
- Md. Milon Islam
- Centre for Pattern Analysis and Machine IntelligenceDepartment of Electrical and Computer EngineeringUniversity of WaterlooWaterlooONN2L 3G1Canada
| | - Fakhri Karray
- Centre for Pattern Analysis and Machine IntelligenceDepartment of Electrical and Computer EngineeringUniversity of WaterlooWaterlooONN2L 3G1Canada
| | - Reda Alhajj
- Department of Computer ScienceUniversity of CalgaryCalgaryABT2N 1N4Canada
| | - Jia Zeng
- Institute for Personalized Cancer TherapyMD Anderson Cancer CenterHoustonTX77030USA
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1877
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Saood A, Hatem I. COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Med Imaging 2021; 21:19. [PMID: 33557772 PMCID: PMC7870362 DOI: 10.1186/s12880-020-00529-5] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Accepted: 11/24/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung images of such patients. Two structurally-different deep learning techniques, SegNet and U-NET, are investigated for semantically segmenting infected tissue regions in CT lung images. METHODS We propose to use two known deep learning networks, SegNet and U-NET, for image tissue classification. SegNet is characterized as a scene segmentation network and U-NET as a medical segmentation tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using seventy-two data images, validated on ten images, and tested against the left eighteen images. Several statistical scores are calculated for the results and tabulated accordingly. RESULTS The results show the superior ability of SegNet in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the U-NET shows better results as a multi-class segmentor (with 0.91 mean accuracy). CONCLUSION Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would not only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize the population treatment accordingly. We propose computer-based techniques that prove to be reliable as detectors for infected tissue in lung CT scans. The availability of such a method in today's pandemic would help automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.
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Affiliation(s)
- Adnan Saood
- Mechatronics Program for the Distinguished, Tishreen University, Distinction and Creativity Agency, Latakia, Syria
| | - Iyad Hatem
- Mechatronics Program for the Distinguished, Tishreen University, Distinction and Creativity Agency, Latakia, Syria
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1878
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DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images. Appl Soft Comput 2021; 103:107160. [PMID: 33584157 PMCID: PMC7868740 DOI: 10.1016/j.asoc.2021.107160] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 01/09/2021] [Accepted: 01/28/2021] [Indexed: 02/06/2023]
Abstract
The new coronavirus, known as COVID-19, first emerged in Wuhan, China, and since then has been transmitted to the whole world. Around 34 million people have been infected with COVID-19 virus so far, and nearly 1 million have died as a result of the virus. Resource shortages such as test kits and ventilator have arisen in many countries as the number of cases have increased beyond the control. Therefore, it has become very important to develop deep learning-based applications that automatically detect COVID-19 cases using chest X-ray images to assist specialists and radiologists in diagnosis. In this study, we propose a new approach based on deep LSTM model to automatically identify COVID-19 cases from X-ray images. Contrary to the transfer learning and deep feature extraction approaches, the deep LSTM model is an architecture, which is learned from scratch. Besides, the Sobel gradient and marker-controlled watershed segmentation operations are applied to raw images for increasing the performance of proposed model in the pre-processing stage. The experimental studies were performed on a combined public dataset constituted by gathering COVID-19, pneumonia and normal (healthy) chest X-ray images. The dataset was randomly separated into two sections as training and testing data. For training and testing, these separations were performed with the rates of 80%-20%, 70%-30% and 60%-40%, respectively. The best performance was achieved with 80% training and 20% testing rate. Moreover, the success rate was 100% for all performance criteria, which composed of accuracy, sensitivity, specificity and F-score. Consequently, the proposed model with pre-processing images ensured promising results on a small dataset compared to big data. Generally, the proposed model can significantly improve the present radiology based approaches and it can be very useful application for radiologists and specialists to help them in detection, quantity determination and tracing of COVID-19 cases throughout the pandemic.
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1879
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Zhong A, Li X, Wu D, Ren H, Kim K, Kim Y, Buch V, Neumark N, Bizzo B, Tak WY, Park SY, Lee YR, Kang MK, Park JG, Kim BS, Chung WJ, Guo N, Dayan I, Kalra MK, Li Q. Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19. Med Image Anal 2021; 70:101993. [PMID: 33711739 PMCID: PMC8032481 DOI: 10.1016/j.media.2021.101993] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 01/19/2021] [Accepted: 02/01/2021] [Indexed: 12/13/2022]
Abstract
In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public health crisis of the novel coronavirus disease 2019 (COVID-19) pandemic. Chest radiograph (CXR) has been playing a crucial role in COVID-19 patient triaging, diagnosing and monitoring, particularly in the United States. Considering the mixed and unspecific signals in CXR, an image retrieval model of CXR that provides both similar images and associated clinical information can be more clinically meaningful than a direct image diagnostic model. In this work we develop a novel CXR image retrieval model based on deep metric learning. Unlike traditional diagnostic models which aim at learning the direct mapping from images to labels, the proposed model aims at learning the optimized embedding space of images, where images with the same labels and similar contents are pulled together. The proposed model utilizes multi-similarity loss with hard-mining sampling strategy and attention mechanism to learn the optimized embedding space, and provides similar images, the visualizations of disease-related attention maps and useful clinical information to assist clinical decisions. The model is trained and validated on an international multi-site COVID-19 dataset collected from 3 different sources. Experimental results of COVID-19 image retrieval and diagnosis tasks show that the proposed model can serve as a robust solution for CXR analysis and patient management for COVID-19. The model is also tested on its transferability on a different clinical decision support task for COVID-19, where the pre-trained model is applied to extract image features from a new dataset without any further training. The extracted features are then combined with COVID-19 patient's vitals, lab tests and medical histories to predict the possibility of airway intubation in 72 hours, which is strongly associated with patient prognosis, and is crucial for patient care and hospital resource planning. These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.
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Affiliation(s)
- Aoxiao Zhong
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; School of Engineering and Applied Sciences, Harvard University, Boston, MA, United States
| | - Xiang Li
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Dufan Wu
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Hui Ren
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Kyungsang Kim
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Younggon Kim
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Varun Buch
- MGH & BWH Center for Clinical Data Science, Boston, MA, United States
| | - Nir Neumark
- MGH & BWH Center for Clinical Data Science, Boston, MA, United States
| | - Bernardo Bizzo
- MGH & BWH Center for Clinical Data Science, Boston, MA, United States
| | - Won Young Tak
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Soo Young Park
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Yu Rim Lee
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Min Kyu Kang
- Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, South Korea
| | - Jung Gil Park
- Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, South Korea
| | - Byung Seok Kim
- Department of Internal Medicine, Catholic University of Daegu School of Medicine, Daegu, South Korea
| | - Woo Jin Chung
- Department of Internal Medicine, Keimyung University School of Medicine, Daegu, South Korea
| | - Ning Guo
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Ittai Dayan
- School of Engineering and Applied Sciences, Harvard University, Boston, MA, United States
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; MGH & BWH Center for Clinical Data Science, Boston, MA, United States.
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1880
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Wan Y, Zhou H, Zhang X. An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis. ENTROPY (BASEL, SWITZERLAND) 2021; 23:204. [PMID: 33562309 PMCID: PMC7916048 DOI: 10.3390/e23020204] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 01/27/2021] [Accepted: 02/04/2021] [Indexed: 12/15/2022]
Abstract
The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field.
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Affiliation(s)
- Yuchai Wan
- Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer Science, Beijing Technology and Business University, Beijing 100048, China; (H.Z.); (X.Z.)
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1881
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Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging. HEALTH AND TECHNOLOGY 2021; 11:411-424. [PMID: 33585153 PMCID: PMC7864619 DOI: 10.1007/s12553-021-00520-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 01/11/2021] [Indexed: 12/17/2022]
Abstract
The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.
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1882
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Mukherjee H, Ghosh S, Dhar A, Obaidullah SM, Santosh KC, Roy K. Shallow Convolutional Neural Network for COVID-19 Outbreak Screening Using Chest X-rays. Cognit Comput 2021:1-14. [PMID: 33564340 PMCID: PMC7863062 DOI: 10.1007/s12559-020-09775-9] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/29/2020] [Indexed: 12/18/2022]
Abstract
Among radiological imaging data, Chest X-rays (CXRs) are of great use in observing COVID-19 manifestations. For mass screening, using CXRs, a computationally efficient AI-driven tool is the must to detect COVID-19-positive cases from non-COVID ones. For this purpose, we proposed a light-weight Convolutional Neural Network (CNN)-tailored shallow architecture that can automatically detect COVID-19-positive cases using CXRs, with no false negatives. The shallow CNN-tailored architecture was designed with fewer parameters as compared to other deep learning models. The shallow CNN-tailored architecture was validated using 321 COVID-19-positive CXRs. In addition to COVID-19-positive cases, another set of non-COVID-19 5856 cases (publicly available, source: Kaggle) was taken into account, consisting of normal, viral, and bacterial pneumonia cases. In our experimental tests, to avoid possible bias, 5-fold cross-validation was followed, and both balanced and imbalanced datasets were used. The proposed model achieved the highest possible accuracy of 99.69%, sensitivity of 1.0, where AUC was 0.9995. Furthermore, the reported false positive rate was only 0.0015 for 5856 COVID-19-negative cases. Our results stated that the proposed CNN could possibly be used for mass screening. Using the exact same set of CXR collection, the current results were better than other deep learning models and major state-of-the-art works.
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Affiliation(s)
- Himadri Mukherjee
- Department of Computer Science, West Bengal State University, Kolkata, India
| | | | - Ankita Dhar
- Department of Computer Science, West Bengal State University, Kolkata, India
| | - Sk Md Obaidullah
- Department of Computer Science and Engineering, Aliah University, Kolkata, India
| | - K. C. Santosh
- Department of Computer Science, The University of South Dakota, Vermillion, SD 57069 USA
| | - Kaushik Roy
- Department of Computer Science, West Bengal State University, Kolkata, India
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1883
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Priya C, Sithi Shameem Fathima SMH, Kirubanandasarathy N, Valanarasid A, Safana Begam MH, Aiswarya N. AUTOMATIC OPTIMIZED CNN BASED COVID-19 LUNG INFECTION SEGMENTATION FROM CT IMAGE. MATERIALS TODAY. PROCEEDINGS 2021:S2214-7853(21)00917-2. [PMID: 33564622 PMCID: PMC7862904 DOI: 10.1016/j.matpr.2021.01.820] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 01/25/2021] [Indexed: 11/18/2022]
Abstract
In early 2020, the corona virus disease (COVID-19) has become a global epidemic. The WHO announced the disease as a public health emergency of international importance (PHEIC), and the issue was considered a health emergency. Automated computed tomography (CD) detection of lung infections offers a tremendous opportunity to expand the traditional health approach to resolving COVID-19. But many problems with CT. Facing contaminated areas from fragments, which include greater variability in infectious properties and low-intensity comparison between infections and normal tissues. Moreover, by suppressing the project of an in-depth model, a lot of information cannot be collected over some time.
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Affiliation(s)
- C Priya
- Department of Electronics and Communication Engineering College, Ramanathapuram, Tamilnadu, India
| | | | - N Kirubanandasarathy
- Department of Electronics and Communication Engineering College, Ramanathapuram, Tamilnadu, India
| | - A Valanarasid
- Department of Electronics and Communication Engineering College, Ramanathapuram, Tamilnadu, India
| | - M H Safana Begam
- Department of Electronics and Communication Engineering College, Ramanathapuram, Tamilnadu, India
| | - N Aiswarya
- Department of Electronics and Communication Engineering College, Ramanathapuram, Tamilnadu, India
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1884
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Does Two-Class Training Extract Real Features? A COVID-19 Case Study. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041424] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Diagnosis aid systems that use image analysis are currently very useful due to the large workload of health professionals involved in making diagnoses. In recent years, Convolutional Neural Networks (CNNs) have been used to help in these tasks. For this reason, multiple studies that analyze the detection precision for several diseases have been developed. However, many of these works distinguish between only two classes: healthy and with a specific disease. Based on this premise, in this work, we try to answer the questions: When training an image classification system with only two classes (healthy and sick), does this system extract the specific features of this disease, or does it only obtain the features that differentiate it from a healthy patient? Trying to answer these questions, we analyze the particular case of COVID-19 detection. Many works that classify this disease using X-ray images have been published; some of them use two classes (with and without COVID-19), while others include more classes (pneumonia, SARS, influenza, etc.). In this work, we carry out several classification studies with two classes, using test images that do not belong to those classes, in order to try to answer the previous questions. The first studies indicate problems in these two-class systems when using a third class as a test, being classified inconsistently. Deeper studies show that deep learning systems trained with two classes do not correctly extract the characteristics of pathologies, but rather differentiate the classes based on the physical characteristics of the images. After the discussion, we conclude that these two-class trained deep learning systems are not valid if there are other diseases that cause similar symptoms.
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1885
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Chakraborty M, Dhavale SV, Ingole J. Corona-Nidaan: lightweight deep convolutional neural network for chest X-Ray based COVID-19 infection detection. APPL INTELL 2021; 51:3026-3043. [PMID: 34764582 PMCID: PMC7851642 DOI: 10.1007/s10489-020-01978-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2020] [Indexed: 12/12/2022]
Abstract
The coronavirus COVID-19 pandemic is today's major public health crisis, we have faced since the Second World War. The pandemic is spreading around the globe like a wave, and according to the World Health Organization's recent report, the number of confirmed cases and deaths are rising rapidly. COVID-19 pandemic has created severe social, economic, and political crises, which in turn will leave long-lasting scars. One of the countermeasures against controlling coronavirus outbreak is specific, accurate, reliable, and rapid detection technique to identify infected patients. The availability and affordability of RT-PCR kits remains a major bottleneck in many countries, while handling COVID-19 outbreak effectively. Recent findings indicate that chest radiography anomalies can characterize patients with COVID-19 infection. In this study, Corona-Nidaan, a lightweight deep convolutional neural network (DCNN), is proposed to detect COVID-19, Pneumonia, and Normal cases from chest X-ray image analysis; without any human intervention. We introduce a simple minority class oversampling method for dealing with imbalanced dataset problem. The impact of transfer learning with pre-trained CNNs on chest X-ray based COVID-19 infection detection is also investigated. Experimental analysis shows that Corona-Nidaan model outperforms prior works and other pre-trained CNN based models. The model achieved 95% accuracy for three-class classification with 94% precision and recall for COVID-19 cases. While studying the performance of various pre-trained models, it is also found that VGG19 outperforms other pre-trained CNN models by achieving 93% accuracy with 87% recall and 93% precision for COVID-19 infection detection. The model is evaluated by screening the COVID-19 infected Indian Patient chest X-ray dataset with good accuracy.
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Affiliation(s)
- Mainak Chakraborty
- Defence Institute of Advanced Technology (DIAT), Girinagar Pune, 411025 India
| | | | - Jitendra Ingole
- Smt. Kashibai Navale Medical College and General Hospital, Narhe Pune, 411041 India
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1886
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Sarhan A. Run length encoding based wavelet features for COVID-19 detection in X-rays. BJR Open 2021; 3:20200028. [PMID: 33718765 PMCID: PMC7931407 DOI: 10.1259/bjro.20200028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 01/20/2021] [Accepted: 01/21/2021] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES Introduced in his paper is a novel approach for the recognition of COVID-19 cases in chest X-rays. METHODS The discrete Wavelet transform (DWT) is employed in the proposed system to obtain highly discriminative features from the input chest X-ray image. The selected features are then classified by a support vector machine (SVM) classifier as either normal or COVID-19 cases. The DWT is well-known for its energy compression power. The proposed system uses the DWT to decompose the chest X-ray image into a group of approximation coefficients that contain a small number of high-energy (high-magnitude) coefficients. The proposed system introduces a novel coefficient selection scheme that employs hard thresholding combined with run-length encoding to extract only high-magnitude Wavelet approximation coefficients. These coefficients are utilized as features symbolizing the chest X-ray input image. After applying zero-padding to unify their lengths, the feature vectors are introduced to a SVM which classifies them as either normal or COVID-19 cases. RESULTS The proposed system yields promising results in terms of classification accuracy, which justifies further work in this direction. CONCLUSION The DWT can produce a few features that are highly discriminative. By reducing the dimensionality of the feature space, the proposed system is able to reduce the number of required training images and diminish the space and time complexities of the system. ADVANCES IN KNOWLEDGE Exploiting and reshaping the approximation coefficients can produce discriminative features representing the input image.
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Affiliation(s)
- Ahmad Sarhan
- Department of Computer Engineering, Amman Arab University, Amman, Jordan
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1887
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Riccardi A, Gemignani J, Fernández-Navarro F, Heffernan A. Optimisation of Non-Pharmaceutical Measures in COVID-19 Growth via Neural Networks. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2021; 5:79-91. [PMID: 37982015 PMCID: PMC8769028 DOI: 10.1109/tetci.2020.3046012] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/01/2020] [Accepted: 12/10/2020] [Indexed: 11/21/2023]
Abstract
On [Formula: see text] March, the World Health Organisation declared a pandemic. Through this global spread, many nations have witnessed exponential growth of confirmed cases brought under control by severe mass quarantine or lockdown measures. However, some have, through a different timeline of actions, prevented this exponential growth. Currently as some continue to tackle growth, others attempt to safely lift restrictions whilst avoiding a resurgence. This study seeks to quantify the impact of government actions in mitigating viral transmission of SARS-CoV-2 by a novel soft computing approach that makes concurrent use of a neural network model, to predict the daily slope increase of cumulative infected, and an optimiser, with a parametrisation of the government restriction time series, to understand the best set of mitigating actions. Data for two territories, Italy and Taiwan, have been gathered to model government restrictions in travelling, testing and enforcement of social distance measures as well as people connectivity and adherence to government actions. It is found that a larger and earlier testing campaign with tighter entry restrictions benefit both regions, resulting in significantly less confirmed cases. Interestingly, this scenario couples with an earlier but milder implementation of nationwide restrictions for Italy, thus supporting Taiwan's lack of nationwide lockdown, i.e. earlier government actions could have contained the growth to a degree that a widespread lockdown would have been avoided, or at least delayed. The results, found with a purely data-driven approach, are in line with the main findings of mathematical epidemiological models, proving that the proposed approach has value and that the data alone contains valuable knowledge to inform decision makers.
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Affiliation(s)
- Annalisa Riccardi
- Department of Mechanical and Aerospace EngineeringUniversity of StrathclydeGlasgowG1 1XQU.K.
| | - Jessica Gemignani
- Department of Developmental Psychology and SocialisationUniversità di Padova35131PadovaItaly
- Integrative Neuroscience and Cognition CenterUniversité de Paris & CNRSParisFrance
| | | | - Anna Heffernan
- Department of PhysicsUniversity of GuelphGuelphOntarioN1G 2W1Canada
- Perimeter Institute of Theoretical PhysicsWaterlooOntarioN2L 2Y5Canada
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1888
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Gupta A, Anjum, Gupta S, Katarya R. InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray. Appl Soft Comput 2021; 99:106859. [PMID: 33162872 PMCID: PMC7598372 DOI: 10.1016/j.asoc.2020.106859] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 10/11/2020] [Accepted: 10/27/2020] [Indexed: 02/06/2023]
Abstract
Recently, the whole world became infected by the newly discovered coronavirus (COVID-19). SARS-CoV-2, or widely known as COVID-19, has proved to be a hazardous virus severely affecting the health of people. It causes respiratory illness, especially in people who already suffer from other diseases. Limited availability of test kits as well as symptoms similar to other diseases such as pneumonia has made this disease deadly, claiming the lives of millions of people. Artificial intelligence models are found to be very successful in the diagnosis of various diseases in the biomedical field In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet to compensate for a relatively small amount of training data. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities caused by such diseases in Chest X-ray images of the person infected. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification while achieving an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification. The proposed model achieves an average recall, F1 score, and precision of 99%, 99%, and 99%, respectively on ternary classification, while achieving a 100% precision and a recall of 99% on the binary class., while achieving a 100% precision and a recall of 99% on the COVID class. InstaCovNet-19's ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine.
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Affiliation(s)
- Anunay Gupta
- Department of Electrical Engineering, Delhi Technological University, New Delhi, India
| | - Anjum
- Department of Computer Science, Delhi Technological University, New Delhi, India
| | - Shreyansh Gupta
- Department of Civil Engineering, Delhi Technological University, New Delhi, India
| | - Rahul Katarya
- Department of Computer Science, Delhi Technological University, New Delhi, India
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1889
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Hoeben BA, Pazos M, Albert MH, Seravalli E, Bosman ME, Losert C, Boterberg T, Manapov F, Ospovat I, Milla SM, Abakay CD, Engellau J, Kos G, Supiot S, Bierings M, Janssens GO. Towards homogenization of total body irradiation practices in pediatric patients across SIOPE affiliated centers. A survey by the SIOPE radiation oncology working group. Radiother Oncol 2021; 155:113-119. [DOI: 10.1016/j.radonc.2020.10.032] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/07/2020] [Accepted: 10/21/2020] [Indexed: 02/06/2023]
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1890
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Shaban WM, Rabie AH, Saleh AI, Abo-Elsoud MA. Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network. Appl Soft Comput 2021; 99:106906. [PMID: 33204229 PMCID: PMC7659585 DOI: 10.1016/j.asoc.2020.106906] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 11/02/2020] [Accepted: 11/10/2020] [Indexed: 12/23/2022]
Abstract
COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Recently, the detection of coronavirus (COVID-19) is a critical task for the medical practitioner. Unfortunately, COVID-19 spreads so quickly between people and approaches millions of people worldwide in few months. It is very much essential to quickly and accurately identify the infected people so that prevention of spread can be taken. Although several medical tests have been used to detect certain injuries, the hopefully detection efficiency has not been accomplished yet. In this paper, a new Hybrid Diagnose Strategy (HDS) has been introduced. HDS relies on a novel technique for ranking selected features by projecting them into a proposed Patient Space (PS). A Feature Connectivity Graph (FCG) is constructed which indicates both the weight of each feature as well as the binding degree to other features. The rank of a feature is determined based on two factors; the first is the feature weight, while the second is its binding degree to its neighbors in PS. Then, the ranked features are used to derive the classification model that can classify new persons to decide whether they are infected or not. The classification model is a hybrid model that consists of two classifiers; fuzzy inference engine and Deep Neural Network (DNN). The proposed HDS has been compared against recent techniques. Experimental results have shown that the proposed HDS outperforms the other competitors in terms of the average value of accuracy, precision, recall, and F-measure in which it provides about of 97.658%, 96.756%, 96.55%, and 96.615% respectively. Additionally, HDS provides the lowest error value of 2.342%. Further, the results were validated statistically using Wilcoxon Signed Rank Test and Friedman Test.
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Affiliation(s)
- Warda M Shaban
- Nile higher institute for engineering and technology, Egypt
| | - Asmaa H Rabie
- Computers and Control Dept. Faculty of engineering, Mansoura University, Egypt
| | - Ahmed I Saleh
- Computers and Control Dept. Faculty of engineering, Mansoura University, Egypt
| | - M A Abo-Elsoud
- Electronics and Communication Dept. Faculty of engineering, Mansoura University, Egypt
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1891
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Karthik R, Menaka R, M H. Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN. Appl Soft Comput 2021; 99:106744. [PMID: 32989379 PMCID: PMC7510455 DOI: 10.1016/j.asoc.2020.106744] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 08/27/2020] [Accepted: 09/18/2020] [Indexed: 12/29/2022]
Abstract
COVID-19 is a deadly viral infection that has brought a significant threat to human lives. Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and reinforces coronavirus testing methods in place. While there exist several challenges in manually inferring traces of this viral infection from X-ray, Convolutional Neural Network (CNN) can mine data patterns that capture subtle distinctions between infected and normal X-rays. To enable automated learning of such latent features, a custom CNN architecture has been proposed in this research. It learns unique convolutional filter patterns for each kind of pneumonia. This is achieved by restricting certain filters in a convolutional layer to maximally respond only to a particular class of pneumonia/COVID-19. The CNN architecture integrates different convolution types to aid better context for learning robust features and strengthen gradient flow between layers. The proposed work also visualizes regions of saliency on the X-ray that have had the most influence on CNN's prediction outcome. To the best of our knowledge, this is the first attempt in deep learning to learn custom filters within a single convolutional layer for identifying specific pneumonia classes. Experimental results demonstrate that the proposed work has significant potential in augmenting current testing methods for COVID-19. It achieves an F1-score of 97.20% and an accuracy of 99.80% on the COVID-19 X-ray set.
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Affiliation(s)
- R Karthik
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
- School of Computing Sciences Engineering, Vellore Institute of Technology, Chennai, India
| | - R Menaka
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
- School of Computing Sciences Engineering, Vellore Institute of Technology, Chennai, India
| | - Hariharan M
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
- School of Computing Sciences Engineering, Vellore Institute of Technology, Chennai, India
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1892
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Nayak SR, Nayak DR, Sinha U, Arora V, Pachori RB. Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study. Biomed Signal Process Control 2021; 64:102365. [PMID: 33230398 PMCID: PMC7674150 DOI: 10.1016/j.bspc.2020.102365] [Citation(s) in RCA: 135] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 10/16/2020] [Accepted: 11/16/2020] [Indexed: 01/22/2023]
Abstract
The emergence of Coronavirus Disease 2019 (COVID-19) in early December 2019 has caused immense damage to health and global well-being. Currently, there are approximately five million confirmed cases and the novel virus is still spreading rapidly all over the world. Many hospitals across the globe are not yet equipped with an adequate amount of testing kits and the manual Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test is time-consuming and troublesome. It is hence very important to design an automated and early diagnosis system which can provide fast decision and greatly reduce the diagnosis error. The chest X-ray images along with emerging Artificial Intelligence (AI) methodologies, in particular Deep Learning (DL) algorithms have recently become a worthy choice for early COVID-19 screening. This paper proposes a DL assisted automated method using X-ray images for early diagnosis of COVID-19 infection. We evaluate the effectiveness of eight pre-trained Convolutional Neural Network (CNN) models such as AlexNet, VGG-16, GoogleNet, MobileNet-V2, SqueezeNet, ResNet-34, ResNet-50 and Inception-V3 for classification of COVID-19 from normal cases. Also, comparative analyses have been made among these models by considering several important factors such as batch size, learning rate, number of epochs, and type of optimizers with an aim to find the best suited model. The models have been validated on publicly available chest X-ray images and the best performance is obtained by ResNet-34 with an accuracy of 98.33%. This study will be useful for researchers to think for the design of more effective CNN based models for early COVID-19 detection.
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Affiliation(s)
- Soumya Ranjan Nayak
- Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India
| | - Deepak Ranjan Nayak
- Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India
| | - Utkarsh Sinha
- Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India
| | - Vaibhav Arora
- Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India
| | - Ram Bilas Pachori
- Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, India
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1893
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Al-Umairi RS, Al-Kalbani J, Al-Tai S, Al-Abri A, Al-Kindi F, Kamona A. COVID-19 Associated Pneumonia: A review of chest radiograph and computed tomography findings. Sultan Qaboos Univ Med J 2021; 21:e4-e11. [PMID: 33777418 PMCID: PMC7968910 DOI: 10.18295/squmj.2021.21.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 09/12/2020] [Accepted: 10/17/2020] [Indexed: 01/08/2023] Open
Abstract
Medical imaging, including chest radiography and computed tomography, plays a major role in the diagnosis and follow-up of patients with COVID-19 associated pneumonia. This review aims to summarise current information on this topic based on the existing literature. A search of the Google Scholar (Google LLC, Mountain View, California, USA) and MEDLINE® (National Library of Medicine, Bethesda, Maryland, USA) databases was conducted for articles published until April 2020. A total of 30 articles involving 4,002 patients were identified. The most frequently reported imaging findings were bilateral ground glass and consolidative pulmonary opacities with a predominant lower lobe and peripheral subpleural distribution.
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Affiliation(s)
| | | | - Saqar Al-Tai
- Department of Radiology, Royal Hospital, Muscat, Oman
| | - Ahmed Al-Abri
- Department of Radiology, Royal Hospital, Muscat, Oman
| | | | - Atheel Kamona
- Department of Radiology, Royal Hospital, Muscat, Oman
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1894
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Muñoz L, Kron T, Petasecca M, Bucci J, Jackson M, Metcalfe P, Rosenfeld AB, Biasi G. Consistency of small-field dosimetry, on and off axis, in beam-matched linacs used for stereotactic radiosurgery. J Appl Clin Med Phys 2021; 22:185-193. [PMID: 33440049 PMCID: PMC7882112 DOI: 10.1002/acm2.13160] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 12/08/2020] [Accepted: 12/17/2020] [Indexed: 11/24/2022] Open
Abstract
PURPOSE Stereotactic radiosurgery (SRS) can be delivered with a standard linear accelerator (linac). At institutions having more than one linac, beam matching is common practice. In the literature, there are indications that machine central axis (CAX) matching for broad fields does not guarantee matching of small fields with side ≤2 cm. There is no indication on how matching for broad fields on axis translates to matching small fields off axis. These are of interest to multitarget single-isocenter (MTSI) SRS planning and the present work addresses that gap in the literature. METHODS We used 6 MV flattening filter free (FFF) beams from four Elekta VersaHD® linacs equipped with an Agility™ multileaf collimator (MLC). The linacs were strictly matched for broad fields on CAX. We compared output factors (OPFs) and effective field size, measured concurrently using a novel 2D solid-state dosimeter "Duo" with a spatial resolution of 0.2 mm, in square and rectangular static fields with sides from 0.5 to 2 cm, either on axis or away from it by 5 to 15 cm. RESULTS Among the four linacs, OPF for fields ≥1 × 1 cm2 ranged 1.3% on CAX, whereas off axis a maximum range of 1.9% was observed at 15 cm. A larger variability in OPF was noted for the 0.5 × 0.5 cm2 field, with a range of 5.9% on CAX, which improved to a maximum of 2.3% moving off axis. Two linacs showed greater consistency with a range of 1.4% on CAX and 2.2% at 15 cm off axis. Between linacs, the effective field size varied by <0.04 cm in most cases, both on and off axis. Tighter matching was observed for linacs with a similar focal spot position. CONCLUSIONS Verification of small-field consistency for matched linacs used for SRS is an important task for dosimetric validation. A significant benefit of concurrent measurement of field size and OPF allowed for a comprehensive assessment using a novel diode array. Our study showed the four linacs, strictly matched for broad fields on CAX, were still matched down to a field size of 1 x 1 cm2 on and off axis.
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Affiliation(s)
- Luis Muñoz
- Genesiscare Flinders Private HospitalBedford ParkSAAustralia
- Centre for Medical Radiation PhysicsUniversity of WollongongNSWAustralia
| | - Tomas Kron
- Centre for Medical Radiation PhysicsUniversity of WollongongNSWAustralia
- Peter MacCallum Cancer CentreMelbourneVICAustralia
| | - Marco Petasecca
- Centre for Medical Radiation PhysicsUniversity of WollongongNSWAustralia
| | - Joseph Bucci
- St. George Cancer Care CentreSt George HospitalKogarahNSWAustralia
- Genesiscare Waratah Private HospitalHurstvilleNSWAustralia
| | - Michael Jackson
- Centre for Medical Radiation PhysicsUniversity of WollongongNSWAustralia
- University of New South WalesKensingtonNSWAustralia
| | - Peter Metcalfe
- Centre for Medical Radiation PhysicsUniversity of WollongongNSWAustralia
| | | | - Giordano Biasi
- Centre for Medical Radiation PhysicsUniversity of WollongongNSWAustralia
- Peter MacCallum Cancer CentreMelbourneVICAustralia
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1895
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Bhattacharya S, Reddy Maddikunta PK, Pham QV, Gadekallu TR, Krishnan S SR, Chowdhary CL, Alazab M, Jalil Piran M. Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey. SUSTAINABLE CITIES AND SOCIETY 2021; 65:102589. [PMID: 33169099 PMCID: PMC7642729 DOI: 10.1016/j.scs.2020.102589] [Citation(s) in RCA: 109] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities.
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Affiliation(s)
- Sweta Bhattacharya
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | | | - Quoc-Viet Pham
- Research Institute of Computer, Information and Communication, Pusan National University, Busan 46241, Republic of Korea
| | - Thippa Reddy Gadekallu
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Siva Rama Krishnan S
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Chiranji Lal Chowdhary
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Mamoun Alazab
- College of Engineering, IT & Environment, Charles Darwin University, Australia
| | - Md Jalil Piran
- Department of Computer Science and Engineering, Sejong University, 05006, Seoul, Republic of Korea
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1896
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Mohamed Sameh A, Abbas MA, Hazem M, Abd Elazeem MH. Automativ assessment of systolic cardiac performance using PEP/LVET index. IOP CONFERENCE SERIES: MATERIALS SCIENCE AND ENGINEERING 2021; 1051:012017. [DOI: 10.1088/1757-899x/1051/1/012017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Abstract
Congestive cardiac failure is one of the deadliest diseases in the world, with more than 26 million patients. Echocardiogram and angiography consider as the gold standards for heart failure diagnosis. Nevertheless, they are not commonly used for long-term follow up as they need highly skilled and experienced operator. In this paper, a simple and low-cost system for automatic assessment of systolic cardiac performance using systolic cardiac intervals is presented. The proposed system utilized electrocardiogram (ECG) and phonocardiogram (PCG) to calculate pre-ejection period (PEP) and left ventricle ejection time (LVET). The ratio between PEP and LVET was computed to assess the performance of the systolic cardiac function. ECG and PCG were acquired using a commercial stethoscope which was modified to convert PCG auscultation to electrical signals. ECG and PCG signals were digitized and transferred to a personal computer. A custom MATLAB application was designed to display the acquired ECG and PCG, and to compute PEP, LVET, and PEP/LVET ratio. The system was tested on 17 healthy subjects and results showed high agreement between the systolic heart function status assessed by the proposed system and the corresponding echocardiography results. These results imply that the proposed system could be used for long-term follow up for patients with congestive heart failure.
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1897
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Sharma A, Badea M, Tiwari S, Marty JL. Wearable Biosensors: An Alternative and Practical Approach in Healthcare and Disease Monitoring. Molecules 2021; 26:748. [PMID: 33535493 PMCID: PMC7867046 DOI: 10.3390/molecules26030748] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 01/24/2021] [Accepted: 01/26/2021] [Indexed: 12/18/2022] Open
Abstract
With the increasing prevalence of growing population, aging and chronic diseases continuously rising healthcare costs, the healthcare system is undergoing a vital transformation from the traditional hospital-centered system to an individual-centered system. Since the 20th century, wearable sensors are becoming widespread in healthcare and biomedical monitoring systems, empowering continuous measurement of critical biomarkers for monitoring of the diseased condition and health, medical diagnostics and evaluation in biological fluids like saliva, blood, and sweat. Over the past few decades, the developments have been focused on electrochemical and optical biosensors, along with advances with the non-invasive monitoring of biomarkers, bacteria and hormones, etc. Wearable devices have evolved gradually with a mix of multiplexed biosensing, microfluidic sampling and transport systems integrated with flexible materials and body attachments for improved wearability and simplicity. These wearables hold promise and are capable of a higher understanding of the correlations between analyte concentrations within the blood or non-invasive biofluids and feedback to the patient, which is significantly important in timely diagnosis, treatment, and control of medical conditions. However, cohort validation studies and performance evaluation of wearable biosensors are needed to underpin their clinical acceptance. In the present review, we discuss the importance, features, types of wearables, challenges and applications of wearable devices for biological fluids for the prevention of diseased conditions and real-time monitoring of human health. Herein, we summarize the various wearable devices that are developed for healthcare monitoring and their future potential has been discussed in detail.
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Affiliation(s)
- Atul Sharma
- School of Chemistry, Monash University, Clayton, Melbourne, VIC 3800, Australia
- Department of Pharmaceutical Chemistry, SGT College of Pharmacy, SGT University, Budhera, Gurugram, Haryana 122505, India
| | - Mihaela Badea
- Fundamental, Prophylactic and Clinical Specialties Department, Faculty of Medicine, Transilvania University of Brasov, 500036 Brasov, Romania;
| | - Swapnil Tiwari
- School of Studies in Chemistry, Pt Ravishankar Shukla University, Raipur, CHATTISGARH 492010, India;
| | - Jean Louis Marty
- University of Perpignan via Domitia, 52 Avenue Paul Alduy, CEDEX 9, 66860 Perpignan, France
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1898
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Benameur N, Mahmoudi R, Zaid S, Arous Y, Hmida B, Bedoui MH. SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review. Clin Imaging 2021; 76:6-14. [PMID: 33545517 PMCID: PMC7840409 DOI: 10.1016/j.clinimag.2021.01.019] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 12/30/2020] [Accepted: 01/17/2021] [Indexed: 02/06/2023]
Abstract
Objective SARS-CoV-2 is a worldwide health emergency with unrecognized clinical features. This paper aims to review the most recent medical imaging techniques used for the diagnosis of SARS-CoV-2 and their potential contributions to attenuate the pandemic. Recent researches, including artificial intelligence tools, will be described. Methods We review the main clinical features of SARS-CoV-2 revealed by different medical imaging techniques. First, we present the clinical findings of each technique. Then, we describe several artificial intelligence approaches introduced for the SARS-CoV-2 diagnosis. Results CT is the most accurate diagnostic modality of SARS-CoV-2. Additionally, ground-glass opacities and consolidation are the most common signs of SARS-CoV-2 in CT images. However, other findings such as reticular pattern, and crazy paving could be observed. We also found that pleural effusion and pneumothorax features are less common in SARS-CoV-2. According to the literature, the B lines artifacts and pleural line irregularities are the common signs of SARS-CoV-2 in ultrasound images. We have also stated the different studies, focusing on artificial intelligence tools, to evaluate the SARS-CoV-2 severity. We found that most of the reported works based on deep learning focused on the detection of SARS-CoV-2 from medical images while the challenge for the radiologists is how to differentiate between SARS-CoV-2 and other viral infections with the same clinical features. Conclusion The identification of SARS-CoV-2 manifestations on medical images is a key step in radiological workflow for the diagnosis of the virus and could be useful for researchers working on computer-aided diagnosis of pulmonary infections.
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Affiliation(s)
- Narjes Benameur
- University of Tunis El Manar, Higher Institute of Medical Technologies of Tunis, Laboratory of Biophysics and Medical Technology, Tunis, Tunisia.
| | - Ramzi Mahmoudi
- Université de Monastir - Laboratoire Technologie Imagerie Médicale - LTIM-LR12ES06, Faculté de Médecine de Monastir, 5019, Monastir, Tunisia; Université Paris-Est, Laboratoire d'Informatique Gaspard-Monge, Unité Mixte CNRS-UMLV-ESIEE UMR8049, ESIEE Paris Cité Descartes, BP99, 93162 Noisy Le Grand, France
| | - Soraya Zaid
- Service Imagerie, Centre Hospitalier Escartons Briancon, France
| | - Younes Arous
- Radiology Service, Military Hospital of Instruction of Tunis, Tunisia
| | - Badii Hmida
- Radiology Service, UR12SP40, CHU Fattouma Bourguiba, 5019 Monastir, Tunisia
| | - Mohamed Hedi Bedoui
- Université de Monastir - Laboratoire Technologie Imagerie Médicale - LTIM-LR12ES06, Faculté de Médecine de Monastir, 5019, Monastir, Tunisia
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1899
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Cossio M, Gilardino RE. Would the Use of Artificial Intelligence in COVID-19 Patient Management Add Value to the Healthcare System? Front Med (Lausanne) 2021; 8:619202. [PMID: 33585525 PMCID: PMC7873524 DOI: 10.3389/fmed.2021.619202] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 01/06/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Manuel Cossio
- Artificial Intelligence Master's Program, Faculty of Informatics, Catalonian Polytechnic University, Barcelona, Spain
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1900
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Alafif T, Tehame AM, Bajaba S, Barnawi A, Zia S. Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18031117. [PMID: 33513984 PMCID: PMC7908539 DOI: 10.3390/ijerph18031117] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 01/16/2021] [Accepted: 01/17/2021] [Indexed: 12/13/2022]
Abstract
With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.
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Affiliation(s)
- Tarik Alafif
- Computer Science Department, Jamoum University College, Umm Al-Qura University, Jamoum 25375, Saudi Arabia
- Correspondence:
| | - Abdul Muneeim Tehame
- Department of Software Engineering, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan;
| | - Saleh Bajaba
- Business Administration Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Ahmed Barnawi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Saad Zia
- IT Department, Jeddah Cable Company, Jeddah 31248, Saudi Arabia;
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