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Jahagirdar P, Vaishnav K, Sarathy NA, Singh H, Kumia K, Banerjee A. Role of C-reactive protein, IL-6, and D-dimers in prediction of severity of coronavirus disease 2019: A pilot study. J Oral Maxillofac Pathol 2024; 28:205-210. [PMID: 39157833 PMCID: PMC11329092 DOI: 10.4103/jomfp.jomfp_28_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/17/2024] [Accepted: 03/29/2024] [Indexed: 08/20/2024] Open
Abstract
Background The global outbreak of coronavirus disease 2019 (COVID-19) presents numerous obstacles for healthcare professionals. The present study aimed to evaluate and compare the role of serum biomarkers like- C-reactive protein (CRP), interleukin-6 (IL-6), and D-dimers in the severity of COVID-19 infection. Methodology A cross-sectional, observational retrospective pilot study was conducted in Udaipur, Rajasthan, wherein data was collected from 250 subjects, out of which, data of 100 subjects were included as per the inclusion criteria. The data was recorded retrospectively among the health professionals via Google Forms in Udaipur, Rajasthan. Results There were 1 (1%), 3 (3%), 31 (31%) and 65 (65%) participants with minor elevation (0.3-1.0), moderate elevation (1-10), marked elevation (10-50) and severe elevation (>50) of CRP respectively. The difference between the groups was statistically highly significant with a significantly higher number of study participants with a severe elevation of CRP levels (χ2 = 107.84, P < 0.001). The results showed that there was a significant difference between the groups with IL6 in 0-7 range while 96 (96%) study participants had >7 IL6, and the difference was statistically highly significant (2 = 84.640, P 0.001). Conclusion In conclusion, the existing body of research indicates a discernible correlation between COVID-19 infection and the fluctuation of biomarker levels. This supplement has the potential to be utilised in clinical practice as a means of informing treatment decisions and determining the necessity of admission to the intensive care unit (ICU).
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Affiliation(s)
- Pramod Jahagirdar
- Department of Dentistry, Karnavati School of Research, Gandhinagar, Gujarat, India
| | - Kalpesh Vaishnav
- Department of Prosthodontics, Crown, and Bridge, Karnavati School of Dentistry, Gujarat, India
| | - Niharika Abhay Sarathy
- Department of Oral and Maxillofacial Pathology, R.R. Dental College and Hospital, Udaipur, Rajasthan, India
| | - Harneet Singh
- Department of Oral Medicine and Radiology, Post Graduate Institute of Dental Sciences, Rohtak, Haryana, India
| | - Komal Kumia
- Department of Oral Medicine and Radiology, Post Graduate Institute of Dental Sciences, Rohtak, Haryana, India
| | - Abhishek Banerjee
- Department of Oral and Maxillofacial Pathology, Awadh Dental College and Hospital, Jamshedpur, Jharkhand, India
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Ippolito D, Maino C, Gandola D, Franco PN, Miron R, Barbu V, Bologna M, Corso R, Breaban ME. Artificial Intelligence Applied to Chest X-ray: A Reliable Tool to Assess the Differential Diagnosis of Lung Pneumonia in the Emergency Department. Diseases 2023; 11:171. [PMID: 37987282 PMCID: PMC10660530 DOI: 10.3390/diseases11040171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND Considering the large number of patients with pulmonary symptoms admitted to the emergency department daily, it is essential to diagnose them correctly. It is necessary to quickly solve the differential diagnosis between COVID-19 and typical bacterial pneumonia to address them with the best management possible. In this setting, an artificial intelligence (AI) system can help radiologists detect pneumonia more quickly. METHODS We aimed to test the diagnostic performance of an AI system in detecting COVID-19 pneumonia and typical bacterial pneumonia in patients who underwent a chest X-ray (CXR) and were admitted to the emergency department. The final dataset was composed of three sub-datasets: the first included all patients positive for COVID-19 pneumonia (n = 1140, namely "COVID-19+"), the second one included all patients with typical bacterial pneumonia (n = 500, "pneumonia+"), and the third one was composed of healthy subjects (n = 1000). Two radiologists were blinded to demographic, clinical, and laboratory data. The developed AI system was used to evaluate all CXRs randomly and was asked to classify them into three classes. Cohen's κ was used for interrater reliability analysis. The AI system's diagnostic accuracy was evaluated using a confusion matrix, and 95%CIs were reported as appropriate. RESULTS The interrater reliability analysis between the most experienced radiologist and the AI system reported an almost perfect agreement for COVID-19+ (κ = 0.822) and pneumonia+ (κ = 0.913). We found 96% sensitivity (95% CIs = 94.9-96.9) and 79.8% specificity (76.4-82.9) for the radiologist and 94.7% sensitivity (93.4-95.8) and 80.2% specificity (76.9-83.2) for the AI system in the detection of COVID-19+. Moreover, we found 97.9% sensitivity (98-99.3) and 88% specificity (83.5-91.7) for the radiologist and 97.5% sensitivity (96.5-98.3) and 83.9% specificity (79-87.9) for the AI system in the detection of pneumonia+ patients. Finally, the AI system reached an accuracy of 93.8%, with a misclassification rate of 6.2% and weighted-F1 of 93.8% in detecting COVID+, pneumonia+, and healthy subjects. CONCLUSIONS The AI system demonstrated excellent diagnostic performance in identifying COVID-19 and typical bacterial pneumonia in CXRs acquired in the emergency setting.
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Affiliation(s)
- Davide Ippolito
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
- School of Medicine, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy
| | - Cesare Maino
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
| | - Davide Gandola
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
| | - Paolo Niccolò Franco
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
| | - Radu Miron
- Sentic Lab, Strada Elena Doamna 20, 700398 Iași, Romania; (R.M.); (V.B.)
| | - Vlad Barbu
- Sentic Lab, Strada Elena Doamna 20, 700398 Iași, Romania; (R.M.); (V.B.)
| | | | - Rocco Corso
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
| | - Mihaela Elena Breaban
- Faculty of Computer Science, “Alexandru Ioan Cuza” University of Iasi, Strada General Henri Mathias Berthelot 16, 700483 Iași, Romania
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3
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Hussein HI, Mohammed AO, Hassan MM, Mstafa RJ. Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images. EXPERT SYSTEMS WITH APPLICATIONS 2023; 223:119900. [PMID: 36969370 PMCID: PMC10023206 DOI: 10.1016/j.eswa.2023.119900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 03/05/2023] [Accepted: 03/15/2023] [Indexed: 06/18/2023]
Abstract
Hundreds of millions of people worldwide have recently been infected by the novel Coronavirus disease (COVID-19), causing significant damage to the health, economy, and welfare of the world's population. Moreover, the unprecedented number of patients with COVID-19 has placed a massive burden on healthcare centers, making timely and rapid diagnosis challenging. A crucial step in minimizing the impact of such problems is to automatically detect infected patients and place them under special care as quickly as possible. Deep learning algorithms, such as Convolutional Neural Networks (CNN), can be used to meet this need. Despite the desired results, most of the existing deep learning-based models were built on millions of parameters (weights), which are not applicable to devices with limited resources. Inspired by such fact, in this research, we developed two new lightweight CNN-based diagnostic models for the automatic and early detection of COVID-19 subjects from chest X-ray images. The first model was built for binary classification (COVID-19 and Normal), whereas the second one was built for multiclass classification (COVID-19, viral pneumonia, or normal). The proposed models were tested on a relatively large dataset of chest X-ray images, and the results showed that the accuracy rates of the 2- and 3-class-based classification models are 98.55% and 96.83%, respectively. The results also revealed that our models achieved competitive performance compared with the existing heavyweight models while significantly reducing cost and memory requirements for computing resources. With these findings, we can indicate that our models are helpful to clinicians in making insightful diagnoses of COVID-19 and are potentially easily deployable on devices with limited computational power and resources.
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Affiliation(s)
- Haval I Hussein
- Department of Computer Science, Faculty of Science, University of Zakho. Zakho, Kurdistan Region, Iraq
| | - Abdulhakeem O Mohammed
- Department of Information Technology Management, Technical College of Administration, Duhok Polytechnic University, Duhok, Iraq
| | - Masoud M Hassan
- Department of Computer Science, Faculty of Science, University of Zakho. Zakho, Kurdistan Region, Iraq
| | - Ramadhan J Mstafa
- Department of Computer Science, Faculty of Science, University of Zakho. Zakho, Kurdistan Region, Iraq
- Department of Computer Science, College of Science, Nawroz University, Duhok, Kurdistan Region, Iraq
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4
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Mozaffari J, Amirkhani A, Shokouhi SB. A survey on deep learning models for detection of COVID-19. Neural Comput Appl 2023; 35:1-29. [PMID: 37362568 PMCID: PMC10224665 DOI: 10.1007/s00521-023-08683-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 05/10/2023] [Indexed: 06/28/2023]
Abstract
The spread of the COVID-19 started back in 2019; and so far, more than 4 million people around the world have lost their lives to this deadly virus and its variants. In view of the high transmissibility of the Corona virus, which has turned this disease into a global pandemic, artificial intelligence can be employed as an effective tool for an earlier detection and treatment of this illness. In this review paper, we evaluate the performance of the deep learning models in processing the X-Ray and CT-Scan images of the Corona patients' lungs and describe the changes made to these models in order to enhance their Corona detection accuracy. To this end, we introduce the famous deep learning models such as VGGNet, GoogleNet and ResNet and after reviewing the research works in which these models have been used for the detection of COVID-19, we compare the performances of the newer models such as DenseNet, CapsNet, MobileNet and EfficientNet. We then present the deep learning techniques of GAN, transfer learning, and data augmentation and examine the statistics of using these techniques. Here, we also describe the datasets introduced since the onset of the COVID-19. These datasets contain the lung images of Corona patients, healthy individuals, and the patients with non-Corona pulmonary diseases. Lastly, we elaborate on the existing challenges in the use of artificial intelligence for COVID-19 detection and the prospective trends of using this method in similar situations and conditions. Supplementary Information The online version contains supplementary material available at 10.1007/s00521-023-08683-x.
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Affiliation(s)
- Javad Mozaffari
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
| | - Abdollah Amirkhani
- School of Automotive Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
| | - Shahriar B. Shokouhi
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
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5
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Dabbagh R, Jamal A, Bhuiyan Masud JH, Titi MA, Amer YS, Khayat A, Alhazmi TS, Hneiny L, Baothman FA, Alkubeyyer M, Khan SA, Temsah MH. Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review. Cureus 2023; 15:e38373. [PMID: 37265897 PMCID: PMC10230599 DOI: 10.7759/cureus.38373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2023] [Indexed: 06/03/2023] Open
Abstract
During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.
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Affiliation(s)
- Rufaidah Dabbagh
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Amr Jamal
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | | | - Maher A Titi
- Quality Management Department, King Saud University Medical City, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Yasser S Amer
- Pediatrics, Quality Management Department, King Saud University Medical City, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Afnan Khayat
- Health Information Management Department, Prince Sultan Military College of Health Sciences, Al Dhahran, SAU
| | - Taha S Alhazmi
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Layal Hneiny
- Medicine, Wegner Health Sciences Library, University of South Dakota, Vermillion, USA
| | - Fatmah A Baothman
- Department of Information Systems, King Abdulaziz University, Jeddah, SAU
| | | | - Samina A Khan
- School of Computer Sciences, Universiti Sains Malaysia, Penang, MYS
| | - Mohamad-Hani Temsah
- Pediatric Intensive Care Unit, Department of Pediatrics, King Saud University, Riyadh, SAU
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6
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Pfeuffer N, Baum L, Stammer W, Abdel-Karim BM, Schramowski P, Bucher AM, Hügel C, Rohde G, Kersting K, Hinz O. Explanatory Interactive Machine Learning. BUSINESS & INFORMATION SYSTEMS ENGINEERING 2023. [PMCID: PMC10119840 DOI: 10.1007/s12599-023-00806-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 01/17/2023] [Indexed: 11/22/2023]
Abstract
The most promising standard machine learning methods can deliver highly accurate classification results, often outperforming standard white-box methods. However, it is hardly possible for humans to fully understand the rationale behind the black-box results, and thus, these powerful methods hamper the creation of new knowledge on the part of humans and the broader acceptance of this technology. Explainable Artificial Intelligence attempts to overcome this problem by making the results more interpretable, while Interactive Machine Learning integrates humans into the process of insight discovery. The paper builds on recent successes in combining these two cutting-edge technologies and proposes how Explanatory Interactive Machine Learning (XIL) is embedded in a generalizable Action Design Research (ADR) process – called XIL-ADR. This approach can be used to analyze data, inspect models, and iteratively improve them. The paper shows the application of this process using the diagnosis of viral pneumonia, e.g., Covid-19, as an illustrative example. By these means, the paper also illustrates how XIL-ADR can help identify shortcomings of standard machine learning projects, gain new insights on the part of the human user, and thereby can help to unlock the full potential of AI-based systems for organizations and research.
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Affiliation(s)
- Nicolas Pfeuffer
- Information Systems and Information Management, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Lorenz Baum
- Information Systems and Information Management, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Wolfgang Stammer
- Machine Learning Group, Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany
| | - Benjamin M. Abdel-Karim
- Information Systems and Information Management, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Patrick Schramowski
- Machine Learning Group, Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany
| | - Andreas M. Bucher
- Diagnostic and Interventional Radiology, Center of Radiology, Hospital of the Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Christian Hügel
- Pneumology and Allergology, Center of Internal Medicine, Hospital of the Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Gernot Rohde
- Pneumology and Allergology, Center of Internal Medicine, Hospital of the Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Kristian Kersting
- Machine Learning Group, Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany
| | - Oliver Hinz
- Information Systems and Information Management, Goethe University Frankfurt, Frankfurt am Main, Germany
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7
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Ren K, Hong G, Chen X, Wang Z. A COVID-19 medical image classification algorithm based on Transformer. Sci Rep 2023; 13:5359. [PMID: 37005476 PMCID: PMC10067012 DOI: 10.1038/s41598-023-32462-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 03/28/2023] [Indexed: 04/04/2023] Open
Abstract
Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. This paper proposes a novel deep learning network based on ResNet-50 merged transformer named RMT-Net. On the backbone of ResNet-50, it uses Transformer to capture long-distance feature information, adopts convolutional neural networks and depth-wise convolution to obtain local features, reduce the computational cost and acceleration the detection process. The RMT-Net includes four stage blocks to realize the feature extraction of different receptive fields. In the first three stages, the global self-attention method is adopted to capture the important feature information and construct the relationship between tokens. In the fourth stage, the residual blocks are used to extract the details of feature. Finally, a global average pooling layer and a fully connected layer perform classification tasks. Training, verification and testing are carried out on self-built datasets. The RMT-Net model is compared with ResNet-50, VGGNet-16, i-CapsNet and MGMADS-3. The experimental results show that the RMT-Net model has a Test_ acc of 97.65% on the X-ray image dataset, 99.12% on the CT image dataset, which both higher than the other four models. The size of RMT-Net model is only 38.5 M, and the detection speed of X-ray image and CT image is 5.46 ms and 4.12 ms per image, respectively. It is proved that the model can detect and classify COVID-19 with higher accuracy and efficiency.
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Affiliation(s)
- Keying Ren
- College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, 300222, China
| | - Geng Hong
- College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, 300222, China
| | - Xiaoyan Chen
- College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, 300222, China.
| | - Zichen Wang
- College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, 300222, China
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8
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Bhosale YH, Patnaik KS. Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-54. [PMID: 37362676 PMCID: PMC10015538 DOI: 10.1007/s11042-023-15029-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 02/01/2023] [Accepted: 02/27/2023] [Indexed: 06/28/2023]
Abstract
This review investigates how Deep Machine Learning (DML) has dealt with the Covid-19 epidemic and provides recommendations for future Covid-19 research. Despite the fact that vaccines for this epidemic have been developed, DL methods have proven to be a valuable asset in radiologists' arsenals for the automated assessment of Covid-19. This detailed review debates the techniques and applications developed for Covid-19 findings using DL systems. It also provides insights into notable datasets used to train neural networks, data partitioning, and various performance measurement metrics. The PRISMA taxonomy has been formed based on pretrained(45 systems) and hybrid/custom(17 systems) models with radiography modalities. A total of 62 systems with respect to X-ray(32), CT(19), ultrasound(7), ECG(2), and genome sequence(2) based modalities as taxonomy are selected from the studied articles. We originate by valuing the present phase of DL and conclude with significant limitations. The restrictions contain incomprehensibility, simplification measures, learning from incomplete labeled data, and data secrecy. Moreover, DML can be utilized to detect and classify Covid-19 from other COPD illnesses. The proposed literature review has found many DL-based systems to fight against Covid19. We expect this article will assist in speeding up the procedure of DL for Covid-19 researchers, including medical, radiology technicians, and data engineers.
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Affiliation(s)
- Yogesh H. Bhosale
- Computer Science and Engineering Department, Birla Institute of Technology, Mesra, Ranchi, India
| | - K. Sridhar Patnaik
- Computer Science and Engineering Department, Birla Institute of Technology, Mesra, Ranchi, India
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9
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Aslani S, Jacob J. Utilisation of deep learning for COVID-19 diagnosis. Clin Radiol 2023; 78:150-157. [PMID: 36639173 PMCID: PMC9831845 DOI: 10.1016/j.crad.2022.11.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 01/12/2023]
Abstract
The COVID-19 pandemic that began in 2019 has resulted in millions of deaths worldwide. Over this period, the economic and healthcare consequences of COVID-19 infection in survivors of acute COVID-19 infection have become apparent. During the course of the pandemic, computer analysis of medical images and data have been widely used by the medical research community. In particular, deep-learning methods, which are artificial intelligence (AI)-based approaches, have been frequently employed. This paper provides a review of deep-learning-based AI techniques for COVID-19 diagnosis using chest radiography and computed tomography. Thirty papers published from February 2020 to March 2022 that used two-dimensional (2D)/three-dimensional (3D) deep convolutional neural networks combined with transfer learning for COVID-19 detection were reviewed. The review describes how deep-learning methods detect COVID-19, and several limitations of the proposed methods are highlighted.
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Affiliation(s)
- S Aslani
- Centre for Medical Image Computing and Department of Respiratory Medicine, University College London, London, UK.
| | - J Jacob
- Centre for Medical Image Computing and Department of Respiratory Medicine, University College London, London, UK
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10
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Gomes JC, de Santana MA, Masood AI, de Lima CL, dos Santos WP. COVID-19's influence on cardiac function: a machine learning perspective on ECG analysis. Med Biol Eng Comput 2023; 61:1057-1081. [PMID: 36662377 PMCID: PMC9854415 DOI: 10.1007/s11517-023-02773-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 01/05/2023] [Indexed: 01/21/2023]
Abstract
In December 2019, the spread of the SARS-CoV-2 virus to the world gave rise to probably the biggest public health problem in the world: the COVID-19 pandemic. Initially seen only as a disease of the respiratory system, COVID-19 is actually a blood disease with effects on the respiratory tract. Considering its influence on hematological parameters, how does COVID-19 affect cardiac function? Is it possible to support the clinical diagnosis of COVID-19 from the automatic analysis of electrocardiography? In this work, we sought to investigate how COVID-19 affects cardiac function using a machine learning approach to analyze electrocardiography (ECG) signals. We used a public database of ECG signals expressed as photographs of printed signals, obtained in the context of emergency care. This database has signals associated with abnormal heartbeat, myocardial infarction, history of myocardial infarction, COVID-19, and healthy heartbeat. We propose a system to support the diagnosis of COVID-19 based on hybrid deep architectures composed of pre-trained convolutional neural networks for feature extraction and Random Forests for classification. We investigated the LeNet, ResNet, and VGG16 networks. The best results were obtained with the VGG16 and Random Forest network with 100 trees, with attribute selection using particle swarm optimization. The instance size has been reduced from 4096 to 773 attributes. In the validation step, we obtained an accuracy of 94%, kappa index of 0.91, and sensitivity, specificity, and area under the ROC curve of 100%. This work showed that the influence of COVID-19 on cardiac function is quite considerable: COVID-19 did not present confusion with any heart disease, nor with signs of healthy individuals. It is also possible to build a solution to support the clinical diagnosis of COVID-19 in the context of emergency care from a non-invasive and technologically scalable solution, based on hybrid deep learning architectures.
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Affiliation(s)
| | | | - Aras Ismael Masood
- Information Technology Department, Technical College of Informatics, Sulaimani Polytechnic University, Sulaymaniyah, Iraq
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11
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Khan A, Khan SH, Saif M, Batool A, Sohail A, Waleed Khan M. A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2165724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Asifullah Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- PIEAS Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan
- Center for Mathematical Sciences, Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan
| | - Saddam Hussain Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat, Pakistan
| | - Mahrukh Saif
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
| | - Asiya Batool
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
| | - Anabia Sohail
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Computer Science, Faculty of Computing & Artificial Intelligence, Air University, Islamabad, Pakistan
| | - Muhammad Waleed Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Mechanical and Aerospace Engineering, Columbus, OH, USA
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12
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Ullah N, Khan JA, El-Sappagh S, El-Rashidy N, Khan MS. A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network. Diagnostics (Basel) 2023; 13:diagnostics13010162. [PMID: 36611454 PMCID: PMC9818310 DOI: 10.3390/diagnostics13010162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/21/2022] [Accepted: 12/28/2022] [Indexed: 01/05/2023] Open
Abstract
Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs, computed tomography (CT) scan, and electrocardiogram (ECG) trace images are the most widely known for early discovery and analysis of the coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i.e., binary, three-class, and four-class classifications. In addition, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i.e., binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 Radiography Database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms state-of-the-art DL approaches, such as Squeezenet, Alexnet, and Darknet19, by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.
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Affiliation(s)
- Naeem Ullah
- Department of Software Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan
| | - Javed Ali Khan
- Department of Software Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan
- Correspondence: or (J.A.K.); (S.E.-S.)
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
- Correspondence: or (J.A.K.); (S.E.-S.)
| | - Nora El-Rashidy
- Department of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafr Elsheikh 33516, Egypt
| | - Mohammad Sohail Khan
- Department of Computer Software Engineering, University of Engineering and Technology Mardan, Mardan 23200, Pakistan
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13
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Ahmed I, Jeon G, Chehri A. An IoT-enabled smart health care system for screening of COVID-19 with multi layers features fusion and selection. COMPUTING 2023; 105. [PMCID: PMC8743102 DOI: 10.1007/s00607-021-00992-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Advancement of smart medical sensors, devices, cloud computing, and health care technologies is getting remarkable attention from academia and the health care industry. As, Internet of things (IoT) has been recognized as one of the promising research topics in the domain of health care, particularly in medical image processing. Researchers utilized various machine and deep learning techniques along with artificial intelligence for analyzing medical images. These developed techniques are used to detect diseases that might assist medical experts in diagnosing diseases at early stages and providing accurate, consistent, effective, and speedy results, and decrease the mortality rate. Nowadays, Coronavirus (COVID-19) has been growing as one of the most rigorous and severe infections and spreading globally. Consequently, an intelligent automated system is required as an active diagnostic choice that might be used to prevent the spread of COVID-19. Thus, this work presented an IoT-enabled smart health care system for the automatic screening and classification of contagious diseases (Pneumonia, COVID-19) in Chest X-ray images. The developed system is based on two different deep learning architectures used with a multi-layers feature fusion and feature selection approach to classify X-ray images of infectious diseases. This work comprises the following steps: to enhance the diversity of the data set, data augmentation is performed, while for feature extraction, deep learning architectures, i.e., VGG-19 and Inception-V3, are used along with transfer learning. For the fusion of extracted features obtained from deep learning architectures, a parallel maximum covariance, and for feature selection, the multi-logistic regression controlled entropy variance approach is applied. For experimentation, a data set is customized containing Chest X-ray images using various publicly available resources. The system provides an overall classification accuracy of 97%.
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Affiliation(s)
- Imran Ahmed
- Center of Excellence in Information Technology, Institute of Management Sciences, 1-A, Sector E-5, Phase VII, Hayatabad, Peshawar, Pakistan
| | - Gwanggil Jeon
- Department of Embedded Systems Engineering, Incheon National University, Incheon, Korea
| | - Abdellah Chehri
- Department of Applied Sciences, University of Quebec in Chicoutimi, 555, boul. de l’Université, Chicoutimi, Québec, G7H 2B1 Canada
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14
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Deep Convolutional Spiking Neural Network optimized with Arithmetic optimization algorithm for lung disease detection using chest X-ray images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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15
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Edrees Almalki Y, Zaffar M, Irfan M, Ali Abbas M, Khalid M, Quraishi K, Ali T, Alshehri F, Khalid Alduraibi S, A. Asiri A, Abd Alkhalik Basha M, Alduraibi A, Saeed M, Rahman S. A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients. INTELLIGENT AUTOMATION & SOFT COMPUTING 2023; 35:163-180. [DOI: 10.32604/iasc.2023.025580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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16
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Bhatele KR, Jha A, Tiwari D, Bhatele M, Sharma S, Mithora MR, Singhal S. COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans. Cognit Comput 2022:1-38. [PMID: 36593991 PMCID: PMC9797382 DOI: 10.1007/s12559-022-10076-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 11/15/2022] [Indexed: 12/30/2022]
Abstract
This review study presents the state-of-the-art machine and deep learning-based COVID-19 detection approaches utilizing the chest X-rays or computed tomography (CT) scans. This study aims to systematically scrutinize as well as to discourse challenges and limitations of the existing state-of-the-art research published in this domain from March 2020 to August 2021. This study also presents a comparative analysis of the performance of four majorly used deep transfer learning (DTL) models like VGG16, VGG19, ResNet50, and DenseNet over the COVID-19 local CT scans dataset and global chest X-ray dataset. A brief illustration of the majorly used chest X-ray and CT scan datasets of COVID-19 patients utilized in state-of-the-art COVID-19 detection approaches are also presented for future research. The research databases like IEEE Xplore, PubMed, and Web of Science are searched exhaustively for carrying out this survey. For the comparison analysis, four deep transfer learning models like VGG16, VGG19, ResNet50, and DenseNet are initially fine-tuned and trained using the augmented local CT scans and global chest X-ray dataset in order to observe their performance. This review study summarizes major findings like AI technique employed, type of classification performed, used datasets, results in terms of accuracy, specificity, sensitivity, F1 score, etc., along with the limitations, and future work for COVID-19 detection in tabular manner for conciseness. The performance analysis of the four majorly used deep transfer learning models affirms that Visual Geometry Group 19 (VGG19) model delivered the best performance over both COVID-19 local CT scans dataset and global chest X-ray dataset.
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Affiliation(s)
| | - Anand Jha
- RJIT BSF Academy, Tekanpur, Gwalior India
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17
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Aljohani A, Alharbe N. A Novel Master-Slave Architecture to Detect COVID-19 in Chest X-ray Image Sequences Using Transfer-Learning Techniques. Healthcare (Basel) 2022; 10:healthcare10122443. [PMID: 36553967 PMCID: PMC9778261 DOI: 10.3390/healthcare10122443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/15/2022] [Accepted: 12/01/2022] [Indexed: 12/10/2022] Open
Abstract
Coronavirus disease, frequently referred to as COVID-19, is a contagious and transmittable disease produced by the SARS-CoV-2 virus. The only solution to tackle this virus and reduce its spread is early diagnosis. Pathogenic laboratory tests such as the polymerase chain reaction (PCR) process take a long time. Also, they regularly produce incorrect results. However, they are still considered the critical standard for detecting the virus. Hence, there is a solid need to evolve computer-assisted diagnosis systems capable of providing quick and low-cost testing in areas where traditional testing procedures are not feasible. This study focuses on COVID-19 detection using X-ray images. The prime objective is to introduce a computer-assisted diagnosis (CAD) system to differentiate COVID-19 from healthy and pneumonia cases using X-ray image sequences. This work utilizes standard transfer-learning techniques for COVID-19 detection. It proposes the master-slave architecture using the most state-of-the-art Densenet201 and Squeezenet1_0 techniques for classifying the COVID-19 virus in chest X-ray image sequences. This paper compares the proposed models with other standard transfer-learning approaches for COVID-19. The performance metrics demonstrate that the proposed approach outperforms standard transfer-learning approaches. This research also fine-tunes hyperparameters and predicts the optimized learning rate to achieve the highest accuracy in the model. After fine-tuning the learning rate, the DenseNet201 model retrieves an accuracy of 83.33%, while the fastest model is SqueezeNet1_0, which retrieves an accuracy of 80%.
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18
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Wang Y, Hargreaves CA. A Review Study of the Deep Learning Techniques used for the Classification of Chest Radiological Images for COVID-19 Diagnosis. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT DATA INSIGHTS 2022. [PMCID: PMC9294035 DOI: 10.1016/j.jjimei.2022.100100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
In the fight against COVID-19, the immediate and accurate screening of infected patients is of great significance. Chest X-Ray (CXR) and Computed Tomography (CT) screening play an important role in the diagnosis of COVID-19. Studies showed that changes occur in Chest Radiological images before the beginning of COVID-19 symptoms for some patients, and the symptoms of COVID-19 and other lung diseases can be similar in their very early stages. Further, it is crucial to effectively distinguish COVID-19 patients from healthy people, and patients with other lung diseases as soon as possible, otherwise inaccurate diagnosis may expose more people to the coronavirus. Many researchers have developed end-to-end deep learning techniques for the classification of COVID-19 patients without manual feature engineering. In this paper, we review the different deep learning techniques that have been used to analyze Chest X-Ray and Computed Tomography scans to classify patients with Covid-19. In addition, we also summarize the common public datasets, challenges, limitations, and possible future work. This review paper is extremely valuable as it confirms that (1) Deep Learning models are effective in classifying chest X-Ray images provided the training dataset is sufficiently large. (2) Data augmentation and generative adversarial networks (GANs) solve the small training dataset problem. (3) Transfer learning methods greatly enhanced the extraction and selection of features that were important for chest image classification. (4) Hyperparameter tuning was valuable for increasing the deep learning model accuracies to generally more than 97%. Our review study helps new researchers identify the gaps and opportunities for further or new research.
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19
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Sinwar D, Dhaka VS, Tesfaye BA, Raghuwanshi G, Kumar A, Maakar SK, Agrawal S. Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:1306664. [PMID: 36304775 PMCID: PMC9581633 DOI: 10.1155/2022/1306664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/06/2022] [Accepted: 09/27/2022] [Indexed: 01/26/2023]
Abstract
Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic.
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Affiliation(s)
- Deepak Sinwar
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | - Vijaypal Singh Dhaka
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | - Biniyam Alemu Tesfaye
- Department of Computer Science, College of Informatics, Bule Hora University, Bule Hora, Ethiopia
| | - Ghanshyam Raghuwanshi
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | - Ashish Kumar
- Department of Mathematics and Statistics, Manipal University Jaipur, Jaipur, India
| | - Sunil Kr. Maakar
- School of Computing Science & Engineering, Galgotias University, Greater Noida, India
| | - Sanjay Agrawal
- Department of Electrical Engineering, Rajkiya Engineering College, Akbarpur, Ambedkar Nagar, India
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20
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Saheb SK, Narayanan B, Rao TVN. ADL-CDF: A Deep Learning Framework for COVID-19 Detection from CT Scans Towards an Automated Clinical Decision Support System. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022; 48:1-13. [PMID: 36212631 PMCID: PMC9531859 DOI: 10.1007/s13369-022-07271-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 09/06/2022] [Indexed: 11/29/2022]
Abstract
The emergence of deep learning has paved to solve many problems in the real world. COVID-19 pandemic, since the late 2019, has been affecting lives of people across the globe. Chest CT scan images are used to detect it and know its severity in patients. The problem with many existing solutions in COVID-19 detection using CT scan images is that inability to detect the infection when it is in initial stages. As the infection can exist on varied scales, there is need for more comprehensive approach that can ascertain the disease at all scales. Towards this end, we proposed a deep learning-based framework known as Automated Deep Learning-based COVID-19 Detection Framework (ADL-CDF). It does not need a human medical expert in diagnosis as it is capable of detecting automatically. The framework is assisted by two algorithms that involve image processing and deep learning. The first algorithm known as Region of Interest (ROI)-based Image Filtering (ROI-IF) which analyses given input CT scan images of a patient and discards the ones where ROI is missing. This algorithm minimizes time taken for processing besides reducing false positive rate. The second algorithm is known as Multi-Scale Feature Selection algorithm that fits into the deep learning framework's pipeline to leverage detection performance of the ADL-CDF. The proposed framework is evaluated against ResNet50V2 and Xception. Our empirical study revealed that our model outperforms the state of the art.
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Affiliation(s)
- Shaik Khasim Saheb
- Department of CSE, Annamalai University, Annamalainagar, Chidambaram, Tamil Nadu India
- Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana India
| | - B. Narayanan
- Department of CSE, Annamalai University, Annamalainagar, Chidambaram, Tamil Nadu India
| | - Thota Venkat Narayana Rao
- Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana India
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21
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Karthik R, Menaka R, Hariharan M, Kathiresan GS. AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions. Ing Rech Biomed 2022; 43:486-510. [PMID: 34336141 PMCID: PMC8312058 DOI: 10.1016/j.irbm.2021.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/14/2021] [Accepted: 07/19/2021] [Indexed: 12/24/2022]
Abstract
Background and objective In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field. Methods The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection. Results The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well. Conclusion This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.
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Affiliation(s)
- R Karthik
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - R Menaka
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - M Hariharan
- School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, India
| | - G S Kathiresan
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
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22
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Comprehensive Survey of Machine Learning Systems for COVID-19 Detection. J Imaging 2022; 8:jimaging8100267. [PMID: 36286361 PMCID: PMC9604704 DOI: 10.3390/jimaging8100267] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/11/2022] [Accepted: 09/20/2022] [Indexed: 01/14/2023] Open
Abstract
The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.
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23
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Pradhan AK, Mishra D, Das K, Obaidat MS, Kumar M. A COVID-19 X-ray image classification model based on an enhanced convolutional neural network and hill climbing algorithms. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:14219-14237. [PMID: 36185320 PMCID: PMC9513301 DOI: 10.1007/s11042-022-13826-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/17/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
The classification of medical images is significant among researchers and physicians for the early identification and clinical treatment of many disorders. Though, traditional classifiers require more time and effort for feature extraction and reduction from images. To overcome this problem, there is a need for a new deep learning method known as Convolution Neural Network (CNN), which shows the high performance and self-learning capabilities. In this paper,to classify whether a chest X-ray (CXR) image shows pneumonia (Normal) or COVID-19 illness, a test-bed analysis has been carried out between pre-trained CNN models like Visual Geometry Group (VGG-16), VGG-19, Inception version 3 (INV3), Caps Net, DenseNet121, Residual Neural Network with 50 deep layers (ResNet50), Mobile-Net and proposed CNN classifier. It has been observed that, in terms of accuracy, the proposed CNN model appears to be potentially superior to others. Additionally, in order to increase the performance of the CNN classifier, a nature-inspired optimization method known as Hill-Climbing Algorithm based CNN (CNN-HCA) model has been proposed to enhance the CNN model's parameters. The proposed CNN-HCA model performance is tested using a simulation study and contrasted to existing hybridized classifiers like as Particle Swarm Optimization (CNN-PSO) and CNN-Jaya. The proposed CNN-HCA model is compared with peer reviewed works in the same domain. The CXR dataset, which is freely available on the Kaggle repository, was used for all experimental validations. In terms of Receiver Operating Characteristic Curve (ROC), Area Under the ROC Curve (AUC), sensitivity, specificity, F-score, and accuracy, the simulation findings show that the CNN-HCA is possibly superior than existing hybrid approaches. Each method employs a k-fold stratified cross-validation strategy to reduce over-fitting.
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Affiliation(s)
- Ashwini Kumar Pradhan
- Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to Be University), Khandagiri, Bhubaneswar, 751030 Odisha India
| | - Debahuti Mishra
- Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to Be University), Khandagiri, Bhubaneswar, 751030 Odisha India
| | - Kaberi Das
- Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to Be University), Khandagiri, Bhubaneswar, 751030 Odisha India
| | - Mohammad S. Obaidat
- Distingsuhed Professor, Indian Institute of Technology, Dhanbad, India
- KASIT, University of Jordan, Amman, Jordan
- University of Science and Technology Beijing, Beijing, China
| | - Manoj Kumar
- Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai, United Arab Emirates
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24
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Costa YMG, Silva SA, Teixeira LO, Pereira RM, Bertolini D, Britto AS, Oliveira LS, Cavalcanti GDC. COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers. SENSORS (BASEL, SWITZERLAND) 2022; 22:7303. [PMID: 36236402 PMCID: PMC9570662 DOI: 10.3390/s22197303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/13/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.
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Affiliation(s)
- Yandre M. G. Costa
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Sergio A. Silva
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Lucas O. Teixeira
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | | | - Diego Bertolini
- Departamento Acadêmico de Ciência da Computação, Universidade Tecnológica Federal do Paraná, Campo Mourão 87301-899, Brazil
| | - Alceu S. Britto
- Departmento de Ciência da Computação, Pontifícia Universidade Católica do Paraná, Curitiba 80215-901, Brazil
| | - Luiz S. Oliveira
- Departamento de Informática, Universidade Federal do Paraná, Curitiba 81531-980, Brazil
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25
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A Novel Method for COVID-19 Detection Based on DCNNs and Hierarchical Structure. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2484435. [PMID: 36092785 PMCID: PMC9453086 DOI: 10.1155/2022/2484435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/13/2022] [Accepted: 07/26/2022] [Indexed: 11/20/2022]
Abstract
The worldwide outbreak of the new coronavirus disease (COVID-19) has been declared a pandemic by the World Health Organization (WHO). It has a devastating impact on daily life, public health, and global economy. Due to the highly infectiousness, it is urgent to early screening of suspected cases quickly and accurately. Chest X-ray medical image, as a diagnostic basis for COVID-19, arouses attention from medical engineering. However, due to small lesion difference and lack of training data, the accuracy of detection model is insufficient. In this work, a transfer learning strategy is introduced to hierarchical structure to enhance high-level features of deep convolutional neural networks. The proposed framework consisting of asymmetric pretrained DCNNs with attention networks integrates various information into a wider architecture to learn more discriminative and complementary features. Furthermore, a novel cross-entropy loss function with a penalty term weakens misclassification. Extensive experiments are implemented on the COVID-19 dataset. Compared with the state-of-the-arts, the effectiveness and high performance of the proposed method are demonstrated.
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Smadi AA, Abugabah A, Al-Smadi AM, Almotairi S. SEL-COVIDNET: An intelligent application for the diagnosis of COVID-19 from chest X-rays and CT-scans. INFORMATICS IN MEDICINE UNLOCKED 2022; 32:101059. [PMID: 36033909 PMCID: PMC9398554 DOI: 10.1016/j.imu.2022.101059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 08/17/2022] [Accepted: 08/17/2022] [Indexed: 11/06/2022] Open
Abstract
COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). We include a global average pooling layer, flattening, and two dense layers that are fully connected. The model’s effectiveness is evaluated using balanced and unbalanced COVID-19 radiography datasets. After that, our model’s performance is analyzed using six evaluation measures: accuracy, sensitivity, specificity, precision, F1-score, and Matthew’s correlation coefficient (MCC). Experiments demonstrated that the proposed SEL-COVIDNET with tuned DenseNet121, InceptionResNetV2, and MobileNetV3Large models outperformed the results of comparative SOTA for multi-class classification (COVID-19 vs. No-finding vs. Pneumonia) in terms of accuracy (98.52%), specificity (98.5%), sensitivity (98.5%), precision (98.7%), F1-score (98.7%), and MCC (97.5%). For the COVID-19 vs. No-finding classification, our method had an accuracy of 99.77%, a specificity of 99.85%, a sensitivity of 99.85%, a precision of 99.55%, an F1-score of 99.7%, and an MCC of 99.4%. The proposed model offers an accurate approach for detecting COVID-19 patients, which aids in the containment of the COVID-19 pandemic.
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Affiliation(s)
- Ahmad Al Smadi
- School of Artificial Intelligence, Xidian University, No. 2 South Taibai Road, Xian, 710071, China.,College of Technological Innovation, Zayed University, Abu Dhabi Campus, UAE
| | - Ahed Abugabah
- College of Technological Innovation, Zayed University, Abu Dhabi Campus, UAE
| | - Ahmad Mohammad Al-Smadi
- Department of Computer Science, Al-Balqa Applied University, Ajloun University College, Jordan
| | - Sultan Almotairi
- Faculty of Community College, Majmaah University, Al Majma'ah, Saudi Arabia
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Zhang H, Liang W, Li C, Xiong Q, Shi H, Hu L, Li G. DCML: Deep contrastive mutual learning for COVID-19 recognition. Biomed Signal Process Control 2022; 77:103770. [PMID: 35530170 PMCID: PMC9058053 DOI: 10.1016/j.bspc.2022.103770] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 03/24/2022] [Accepted: 04/27/2022] [Indexed: 01/15/2023]
Abstract
COVID-19 is a form of disease triggered by a new strain of coronavirus. Automatic COVID-19 recognition using computer-aided methods is beneficial for speeding up diagnosis efficiency. Current researches usually focus on a deeper or wider neural network for COVID-19 recognition. And the implicit contrastive relationship between different samples has not been fully explored. To address these problems, we propose a novel model, called deep contrastive mutual learning (DCML), to diagnose COVID-19 more effectively. A multi-way data augmentation strategy based on Fast AutoAugment (FAA) was employed to enrich the original training dataset, which helps reduce the risk of overfitting. Then, we incorporated the popular contrastive learning idea into the conventional deep mutual learning (DML) framework to mine the relationship between diverse samples and created more discriminative image features through a new adaptive model fusion method. Experimental results on three public datasets demonstrate that the DCML model outperforms other state-of-the-art baselines. More importantly, DCML is easier to reproduce and relatively efficient, strengthening its high practicality.
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Affiliation(s)
- Hongbin Zhang
- School of Software, East China Jiaotong University, China
| | - Weinan Liang
- School of Software, East China Jiaotong University, China
| | - Chuanxiu Li
- School of Information Engineering, East China Jiaotong University, China
| | - Qipeng Xiong
- School of Software, East China Jiaotong University, China
| | - Haowei Shi
- School of Software, East China Jiaotong University, China
| | - Lang Hu
- School of Software, East China Jiaotong University, China
| | - Guangli Li
- School of Information Engineering, East China Jiaotong University, China
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Sasikaladevi N. Delaunay triangulation based intelligent system for the diagnosis of covid from the low radiation CXR images. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:1-10. [PMID: 37360780 PMCID: PMC10112999 DOI: 10.1007/s12652-022-04329-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 07/11/2022] [Indexed: 06/28/2023]
Abstract
Covid-19 is a viral infection that causes a profound impact on the lives of the World population. It is a global pandemic spreading across the world in a faster way. It made a global impact on the health, economy, and education system in all the countries. As it is a rapidly spreading disease, prevention demands a fast and accurate diagnosis system. In a highly densely populated country, the demand for fast and affordable early diagnosis is required to reduce the disaster. Within this diagnosis time, the infection spreads rapidly and worsens the infected person's status. To provide a faster and more affordable early diagnosis of covid, posterior-anterior chest radiographs (CXR) are used. Diagnosis of covid from CXR is challenging due to the images' interclass similarity and intraclass variation. This study proposes a deep learning-based robust early diagnosis method for covid. To balance the intraclass variation and interclass similarity in CXR images, the deep fused Delaunay triangulation (DT) is proposed as the CXR has low radiation and unbalanced quality images. The deep features are to be extracted to increase the robustness of the diagnosis method. Without segmentation, the proposed DT algorithm achieves the accurate visualization of the suspicious region in the CXR. The proposed model is trained and tested by the largest benchmark covid-19 radiology dataset with 3616 covid CXR images and 3500 standard CXR images. The performance of the proposed system is analyzed in terms of accuracy, sensitivity, specificity, and AUC. The proposed system yields the highest validation accuracy.
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Affiliation(s)
- N. Sasikaladevi
- Dept. of CSE, School of Computing, SASTRA Deemed University, Thanjavur, TN India
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Sasikaladevi N, Revathi A. Hypergraph Convolutional Neural Network for Fast and Accurate Diagnosis (FAT) of COVID From X-ray Images. INT J PATTERN RECOGN 2022. [DOI: 10.1142/s0218001422570051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Background and objective: The Covid-19 pandemic significantly affects the global population’s fitness and day-to-day life. The necessary action to fight against Covid is to have a fast, accurate and affordable diagnosis system. Most of the diagnostic systems available today have a low detection rate and are time consuming. Hence, there is a demand to design an affordable, accurate, and fast diagnostic system for Covid. The diagnostic system that uses the Convolutional Neural Network (CNN) does not consider the complex correlation of multimodal image data, thus misleading the diagnostic results. Graph Convolutional Network (GCN) provides a better solution for the complex representation of data, as it is modeled based on the pairwise relationship in the image features. Method: Diagnosis of Covid, Parasite, or Lung Tumor from X-ray images needs a more complex representative model. There is a demand to categorize the images grounded on highly complex features. To solve the issue mentioned earlier, this work proposes a Hypergraph- and convolutional neural network-based Fast and Accurate Diagnosis (FAT) system for Covid. The in-depth features are mined using a residual neural network from the X-ray images. The learning-based method optimizes a high-level correlation in the deep structures by constructing it as a hypergraph. Results: The proposed method is assessed based on the Covid dataset. The experimental outcomes show that the proposed system FAT provides the accuracy of 99.8%, sensitivity of 99.5%, and specificity of 99%. It outperforms all the current diagnosis systems for Covid. Conclusion: The proposed deep learning-based model is well suited for Covid diagnosis at the preliminary level. It allows diagnosing Covid by low radiation chest X-ray images with higher accuracy.
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Affiliation(s)
- N. Sasikaladevi
- Department of CSE, School of Computing, SASTRA Deemed University, Thanjavur, India
| | - A. Revathi
- Department of ECE, School of EEE, SASTRA Deemed University, Thanjavur, India
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Aktas K, Ignjatovic V, Ilic D, Marjanovic M, Anbarjafari G. Deep convolutional neural networks for detection of abnormalities in chest X-rays trained on the very large dataset. SIGNAL, IMAGE AND VIDEO PROCESSING 2022; 17:1035-1041. [PMID: 35873389 PMCID: PMC9296894 DOI: 10.1007/s11760-022-02309-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 04/10/2022] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Abstract
One of the main challenges in the current pandemic is the detection of coronavirus. Conventional techniques (PT-PCR) have their limitations such as long response time and limited accessibility. On the other hand, X-ray machines are widely available and they are already digitized in the health systems. Thus, their usage is faster and more available. Therefore, in this research, we evaluate how well deep CNNs do when it comes to classifying normal versus pathological chest X-rays. Compared to the previous research, we trained our network on the largest number of images, 103,468 in total, including 5 classes such as COPD signs, COVID, normal, others and Pneumonia. We achieved COVID accuracy of 97% and overall accuracy of 81%. Additionally, we achieved classification accuracy of 84% for categorization into normal (78%) and abnormal (88%).
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Affiliation(s)
- Kadir Aktas
- iCV Research Lab, Institute of Technology, University of Tartu, 51009 Tartu, Estonia
- iVCV OÜ, 51011 Tartu, Estonia
| | | | - Dragan Ilic
- Singidunum University, Belgrade, 11010 Serbia
| | | | - Gholamreza Anbarjafari
- iCV Research Lab, Institute of Technology, University of Tartu, 51009 Tartu, Estonia
- iVCV OÜ, 51011 Tartu, Estonia
- PwC Advisory, Helsinki, Finland
- Yildiz Technical University, Istanbul, Turkey
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31
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Ho TKK, Gwak J. Feature-level ensemble approach for COVID-19 detection using chest X-ray images. PLoS One 2022; 17:e0268430. [PMID: 35834442 PMCID: PMC9282557 DOI: 10.1371/journal.pone.0268430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 05/01/2022] [Indexed: 01/08/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), also known as the coronavirus disease 2019 (COVID-19), has threatened many human beings around the world and capsized economies at unprecedented magnitudes. Therefore, the detection of this disease using chest X-ray modalities has played a pivotal role in producing fast and accurate medical diagnoses, especially in countries that are unable to afford laboratory testing kits. However, identifying and distinguishing COVID-19 from virtually similar thoracic abnormalities utilizing medical images is challenging because it is time-consuming, demanding, and susceptible to human-based errors. Therefore, artificial-intelligence-driven automated diagnoses, which excludes direct human intervention, may potentially be used to achieve consistently accurate performances. In this study, we aimed to (i) obtain a customized dataset composed of a relatively small number of images collected from publicly available datasets; (ii) present the efficient integration of the shallow handcrafted features obtained from local descriptors, radiomics features specialized for medical images, and deep features aggregated from pre-trained deep learning architectures; and (iii) distinguish COVID-19 patients from healthy controls and pneumonia patients using a collection of conventional machine learning classifiers. By conducting extensive experiments, we demonstrated that the feature-based ensemble approach provided the best classification metrics, and this approach explicitly outperformed schemes that used only either local, radiomic, or deep features. In addition, our proposed method achieved state-of-the-art multi-class classification results compared to the baseline reference for the currently available COVID-19 datasets.
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Affiliation(s)
- Thi Kieu Khanh Ho
- Department of Software, Korea National University of Transportation, Chungju, South Korea
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju, South Korea
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju, South Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju, South Korea
- Department of IT & Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju, South Korea
- * E-mail:
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Kaushik B, Koundal D, Goel N, Zaguia A, Belay A, Turabieh H. Computational Intelligence-Based Method for Automated Identification of COVID-19 and Pneumonia by Utilizing CXR Scans. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7124199. [PMID: 35800691 PMCID: PMC9253872 DOI: 10.1155/2022/7124199] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/24/2022] [Accepted: 04/30/2022] [Indexed: 12/04/2022]
Abstract
Chest X-ray (CXR) scans are emerging as an important diagnostic tool for the early spotting of COVID and other significant lung diseases. The recognition of visual symptoms is difficult and can take longer time by radiologists as CXR provides various signs of viral infection. Therefore, artificial intelligence-based method for automated identification of COVID by utilizing X-ray images has been found to be very promising. In the era of deep learning, effective utilization of existing pretrained generalized models is playing a decisive role in terms of time and accuracy. In this paper, the benefits of weights of existing pretrained model VGG16 and InceptionV3 have been taken. Base model has been created using pretrained models (VGG16 and InceptionV3). The last fully connected (FC) layer has been added as per the number of classes for classification of CXR in binary and multi-class classification by appropriately using transfer learning. Finally, combination of layers is made by integrating the FC layer weights of both the models (VGG16 and InceptionV3). The image dataset used for experimentation consists of healthy, COVID, pneumonia viral, and pneumonia bacterial. The proposed weight fusion method has outperformed the existing models in terms of accuracy, achieved 99.5% accuracy in binary classification over 20 epochs, and 98.2% accuracy in three-class classification over 100 epochs.
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Affiliation(s)
- Bhavana Kaushik
- School of Computer Science, University of Petroleum & Energy Studies, Bidholi, Dehradun, India
| | - Deepika Koundal
- School of Computer Science, University of Petroleum & Energy Studies, Bidholi, Dehradun, India
| | - Neelam Goel
- University Institute of Engineering and Technology, Panjab University, Chandigarh, India
| | - Atef Zaguia
- Department of Information Technology, College of Computing and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Assaye Belay
- Department of Statistics, Mizan-Tepi University, Tepi, Ethiopia
| | - Hamza Turabieh
- Department of Information Technology, College of Computing and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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Mannepalli DP, Namdeo V. An effective detection of COVID-19 using adaptive dual-stage horse herd bidirectional long short-term memory framework. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022; 32:1049-1067. [PMID: 35937036 PMCID: PMC9347606 DOI: 10.1002/ima.22747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 04/05/2022] [Accepted: 04/22/2022] [Indexed: 05/08/2023]
Abstract
COVID-19 is a quickly increasing severe viral disease that affects the human beings as well as animals. The increasing amount of infection and death due to COVID-19 needs timely detection. This work presented an innovative deep learning methodology for the prediction of COVID-19 patients with chest x-ray images. Chest x-ray is the most effective imaging technique for predicting the lung associated diseases. An effective approach with adaptive dual-stage horse herd bidirectional LSTM model is presented for the classification of images into normal, lung opacity, viral pneumonia, and COVID-19. Initially, the input images are preprocessed using modified histogram equalization approach. This is utilized to improve the contrast of the images by changing low-resolution images into high-resolution images. Subsequently, an extended dual tree complex wavelet with trigonometric transform is introduced to extract the high-density features to decrease the complexity of features. Moreover, the dimensionality of the features reduced by adaptive beetle antennae search optimization is utilized. This approach enhances the performance of disease classification by reducing the computational complexity. Finally, an adaptive dual-stage horse herd bidirectional LSTM model is utilized for the classification of images into normal, viral pneumonia, lung opacity, and COVID-19. The implementation platform used in the work is PYTHON. The performance of the presented approach is proved by comparing with the existing approaches in accuracy (99.07%), sensitivity (97.6%), F-measure (97.1%), specificity (99.36%), kappa coefficient (97.7%), precision (98.56%), and area under the receiver operating characteristic curve (99%) for COVID-19 chest x-ray database.
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Affiliation(s)
- Durga Prasad Mannepalli
- Department of Computer Science and EngineeringSarvepalli Radhakrishna UniversityBhopalMadhya PradeshIndia
| | - Varsha Namdeo
- Department of Computer Science and EngineeringSarvepalli Radhakrishna UniversityBhopalMadhya PradeshIndia
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Diagnosing gastrointestinal diseases from endoscopy images through a multi-fused CNN with auxiliary layers, alpha dropouts, and a fusion residual block. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Schneider M. The Role of Biomarkers in Hospitalized COVID-19 Patients With Systemic Manifestations. Biomark Insights 2022; 17:11772719221108909. [PMID: 35783222 PMCID: PMC9243490 DOI: 10.1177/11772719221108909] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/06/2022] [Indexed: 01/08/2023] Open
Abstract
The following article aims to review COVID-19 biomarkers used in hospital
practice. It is apparent that COVID-19 is not simply a pulmonary disease but has
systemic manifestations. For this reason, biomarkers must be used in the
management of diagnosed patients to provide holistic care. Patients with
COVID-19 have been shown to have pulmonary, hepatobiliary, cardiovascular,
neurologic, and renal injury, along with coagulopathy and a distinct cytokine
storm. Biomarkers can effectively inform clinicians of systemic organ injury due
to COVID-19. Furthermore, biomarkers can be used in predictive models for severe
COVID-19 in admitted patients. The utility of doing so is to allow for risk
stratification and utilization of proper treatment protocols. In addition,
COVID-19 biomarkers in the pediatric population are discussed, specifically in
predicting Multisystem Inflammatory Syndrome. Ultimately, biomarkers can be used
as predictive tools to allow clinicians to identify and adequately manage
patients at increased risk for worse outcomes from COVID-19. Both literature
review and anecdotal evidence has shown that severe COVID-19 is a systemic
disease, and understanding associated biomarkers are crucial for hospitalized
patients’ proper clinical decision-making. For example, the cytokine storm
releases inflammatory markers in different organ systems such as the pulmonary,
hepatobiliary, hematological, cardiac, neurological, and renal systems. This
review summarizes the latest research of COVID-19 that can help inform
healthcare professionals how to better mitigate morbidity and mortality
associated with this disease and provides information about certain systemic
biomarkers that can be incorporated into hospital practice to provide more
comprehensive care for hospitalized COIVD-19 patients.
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Affiliation(s)
- Michael Schneider
- University of Queensland Ochsner Clinical School, New Orleans, LA, USA
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36
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A Novel CovidDetNet Deep Learning Model for Effective COVID-19 Infection Detection Using Chest Radiograph Images. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12126269] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The suspected cases of COVID-19 must be detected quickly and accurately to avoid the transmission of COVID-19 on a large scale. Existing COVID-19 diagnostic tests are slow and take several hours to generate the required results. However, on the other hand, most X-rays or chest radiographs only take less than 15 min to complete. Therefore, we can utilize chest radiographs to create a solution for early and accurate COVID-19 detection and diagnosis to reduce COVID-19 patient treatment problems and save time. For this purpose, CovidDetNet is proposed, which comprises ten learnable layers that are nine convolutional layers and one fully-connected layer. The architecture uses two activation functions: the ReLu activation function and the Leaky Relu activation function and two normalization operations that are batch normalization and cross channel normalization, making it a novel COVID-19 detection model. It is a novel deep learning-based approach that automatically and reliably detects COVID-19 using chest radiograph images. Towards this, a fine-grained COVID-19 classification experiment is conducted to identify and classify chest radiograph images into normal, COVID-19 positive, and pneumonia. In addition, the performance of the proposed novel CovidDetNet deep learning model is evaluated on a standard COVID-19 Radiography Database. Moreover, we compared the performance of our approach with hybrid approaches in which we used deep learning models as feature extractors and support vector machines (SVM) as a classifier. Experimental results on the dataset showed the superiority of the proposed CovidDetNet model over the existing methods. The proposed CovidDetNet outperformed the baseline hybrid deep learning-based models by achieving a high accuracy of 98.40%.
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Suganyadevi S, Seethalakshmi V. CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network. WIRELESS PERSONAL COMMUNICATIONS 2022; 126:3279-3303. [PMID: 35756172 PMCID: PMC9206838 DOI: 10.1007/s11277-022-09864-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/29/2022] [Indexed: 06/04/2023]
Abstract
The use of computer-assisted analysis to improve image interpretation has been a long-standing challenge in the medical imaging industry. In terms of image comprehension, Continuous advances in AI (Artificial Intelligence), predominantly in DL (Deep Learning) techniques, are supporting in the classification, Detection, and quantification of anomalies in medical images. DL techniques are the most rapidly evolving branch of AI, and it's recently been successfully pragmatic in a variety of fields, including medicine. This paper provides a classification method for COVID 19 infected X-ray images based on new novel deep CNN model. For COVID19 specified pneumonia analysis, two new customized CNN architectures, CVD-HNet1 (COVID-HybridNetwork1) and CVD-HNet2 (COVID-HybridNetwork2), have been designed. The suggested method utilizes operations based on boundaries and regions, as well as convolution processes, in a systematic manner. In comparison to existing CNNs, the suggested classification method achieves excellent Accuracy 98 percent, F Score 0.99 and MCC 0.97. These results indicate impressive classification accuracy on a limited dataset, with more training examples, much better results can be achieved. Overall, our CVD-HNet model could be a useful tool for radiologists in diagnosing and detecting COVID 19 instances early.
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Affiliation(s)
- S. Suganyadevi
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu 641 407 India
| | - V. Seethalakshmi
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu 641 407 India
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Pandey SK, Bhandari AK, Singh H. A transfer learning based deep learning model to diagnose covid-19 CT scan images. HEALTH AND TECHNOLOGY 2022; 12:845-866. [PMID: 35698586 PMCID: PMC9177227 DOI: 10.1007/s12553-022-00677-4] [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: 03/22/2022] [Accepted: 05/20/2022] [Indexed: 12/15/2022]
Abstract
To save the life of human beings during the pandemic conditions we need an effective automated method to deal with this situation. In pandemic conditions when the available resources becomes insufficient to handle the patient’s load, then we needed some fast and reliable method which analyse the patient medical data with high efficiency and accuracy within time limitations. In this manuscript, an effective and efficient method is proposed for exact diagnosis of the patient whether it is coronavirus disease-2019 (covid-19) positive or negative with the help of deep learning. To find the correct diagnosis with high accuracy we use pre-processed segmented images for the analysis with deep learning. In the first step the X-ray image or computed tomography (CT) of a covid-19 infected person is analysed with various schemes of image segmentation like simple thresholding at 0.3, simple thresholding at 0.6, multiple thresholding (between 26–230) and Otsu’s algorithm. On comparative analysis of all these methods, it is found that the Otsu’s algorithm is a simple and optimum scheme to improve the segmented outcome of binary image for the diagnosis point of view. Otsu’s segmentation scheme gives more precise values in comparison to other methods on the scale of various image quality parameters like accuracy, sensitivity, f-measure, precision, and specificity. For image classification here we use Resnet-50, MobileNet and VGG-16 models of deep learning which gives accuracy 70.24%, 72.95% and 83.18% respectively with non-segmented CT scan images and 75.08%, 80.12% and 99.28% respectively with Otsu’s segmented CT scan images. On a comparative study we find that the VGG-16 models with CT scan image segmented with Otsu’s segmentation gives very high accuracy of 99.28%. On the basis of the diagnosis of the patient firstly we go for an arterial blood gas (ABG) analysis and then on the behalf of this diagnosis and ABG report, the severity level of the patient can be decided and according to this severity level, proper treatment protocols can be followed immediately to save the patient's life. Compared with the existing works, our deep learning based novel method reduces the complexity, takes much less time and has a greater accuracy for exact diagnosis of coronavirus disease-2019 (covid-19).
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Affiliation(s)
- Sanat Kumar Pandey
- Department of Electronics and Communication Engineering, National Institute of Technology Patna, Bihar, India
| | - Ashish Kumar Bhandari
- Department of Electronics and Communication Engineering, National Institute of Technology Patna, Bihar, India
| | - Himanshu Singh
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tiruchippalli, India
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2dCNN-BiCuDNNLSTM: Hybrid Deep-Learning-Based Approach for Classification of COVID-19 X-ray Images. SUSTAINABILITY 2022. [DOI: 10.3390/su14116785] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The coronavirus (COVID-19) is a major global disaster of humankind, in the 21st century. COVID-19 initiates breathing infection, including pneumonia, common cold, sneezing, and coughing. Initial detection becomes crucial, to classify the virus and limit its spread. COVID-19 infection is similar to other types of pneumonia, and it may result in severe pneumonia, with bundles of illness onsets. This research is focused on identifying people affected by COVID-19 at a very early stage, through chest X-ray images. Chest X-ray classification is a beneficial method in the identification, follow up, and evaluation of treatment efficiency, for people with pneumonia. This research, also, considered chest X-ray classification as a basic method to evaluate the existence of lung irregularities in symptomatic patients, alleged for COVID-19 disease. The aim of this research is to classify COVID-19 samples from normal chest X-ray images and pneumonia-affected chest X-ray images of people, for early identification of the disease. This research will help people in diagnosing individuals for viruses and insisting that people receive proper treatment as well as preventive action, to stop the spread of the virus. To provide accurate classification of disease in patients’ chest X-ray images, this research proposed a novel classification model, named 2dCNN-BiCuDNNLSTM, which combines two-dimensional Convolutional Neural Network (CNN) and a Bidirectional CUDA Deep Neural Network Long Short-Term Memory (BiCuDNNLSTM). Deep learning is known for identifying the patterns in available data that will be helpful in accurate classification of disease. The proposed model (2dCNN and BiCuDNNLSTM layers, with proper hyperparameters) can differentiate normal chest X-rays from viral pneumonia and COVID-19 ones, with high accuracy. A total of 6863 X-ray images (JPEG) (1000 COVID-19 patients, 3863 normal cases, and 2000 pneumonia patients) have been engaged, to examine the achievement of the suggested neural network; 80% of the images dataset for every group is received for proposed model training, 10% is accepted for validation, and 10% is accepted for testing. It is observed that the proposed model acquires the towering classification accuracy of 93%. The proposed network is used for predictive analysis, to prompt people regarding the risk of early detection of COVID-19. X-ray images help to classify people with COVID-19 variants and to indicate the severity of disease in the future. This study demonstrates the effectiveness of the proposed CUDA-enabled hybrid deep learning models, to classify the X-ray image data, with a high accuracy of detecting COVID-19. It reveals that the proposed model can be applicable in numerous virus classifications. The chest X-ray classification is a commonly available and reasonable approach, for diagnosing people with lower respiratory signs or suspected COVID-19. Therefore, it is demonstrated that the proposed model has an efficient and promising accomplishment for classifying COVID-19 through X-ray images. The proposed hybrid model can, efficiently, preserve the comprehensive characteristic facts of the image data, for more exceptional concluding classification results than an individual neural network.
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Deep Learning Methods to Reveal Important X-ray Features in COVID-19 Detection: Investigation of Explainability and Feature Reproducibility. REPORTS 2022. [DOI: 10.3390/reports5020020] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
X-ray technology has been recently employed for the detection of the lethal human coronavirus disease 2019 (COVID-19) as a timely, cheap, and helpful ancillary method for diagnosis. The scientific community evaluated deep learning methods to aid in the automatic detection of the disease, utilizing publicly available small samples of X-ray images. In the majority of cases, the results demonstrate the effectiveness of deep learning and suggest valid detection of the disease from X-ray scans. However, little has been investigated regarding the actual findings of deep learning through the image process. In the present study, a large-scale dataset of pulmonary diseases, including COVID-19, was utilized for experiments, aiming to shed light on this issue. For the detection task, MobileNet (v2) was employed, which has been proven very effective in our previous works. Through analytical experiments utilizing feature visualization techniques and altering the input dataset classes, it was suggested that MobileNet (v2) discovers important image findings and not only features. It was demonstrated that MobileNet (v2) is an effective, accurate, and low-computational-cost solution for distinguishing COVID-19 from 12 various other pulmonary abnormalities and normal subjects. This study offers an analysis of image features extracted from MobileNet (v2), aiming to investigate the validity of those features and their medical importance. The pipeline can detect abnormal X-rays with an accuracy of 95.45 ± 1.54% and can distinguish COVID-19 with an accuracy of 89.88 ± 3.66%. The visualized results of the Grad-CAM algorithm provide evidence that the methodology identifies meaningful areas on the images. Finally, the detected image features were reproducible in 98% of the times after repeating the experiment for three times.
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Chetoui M, Akhloufi MA. Explainable Vision Transformers and Radiomics for COVID-19 Detection in Chest X-rays. J Clin Med 2022; 11:3013. [PMID: 35683400 PMCID: PMC9181325 DOI: 10.3390/jcm11113013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/18/2022] [Accepted: 05/23/2022] [Indexed: 01/30/2023] Open
Abstract
The rapid spread of COVID-19 across the globe since its emergence has pushed many countries' healthcare systems to the verge of collapse. To restrict the spread of the disease and lessen the ongoing cost on the healthcare system, it is critical to appropriately identify COVID-19-positive individuals and isolate them as soon as possible. The primary COVID-19 screening test, RT-PCR, although accurate and reliable, has a long turn-around time. More recently, various researchers have demonstrated the use of deep learning approaches on chest X-ray (CXR) for COVID-19 detection. However, existing Deep Convolutional Neural Network (CNN) methods fail to capture the global context due to their inherent image-specific inductive bias. In this article, we investigated the use of vision transformers (ViT) for detecting COVID-19 in Chest X-ray (CXR) images. Several ViT models were fine-tuned for the multiclass classification problem (COVID-19, Pneumonia and Normal cases). A dataset consisting of 7598 COVID-19 CXR images, 8552 CXR for healthy patients and 5674 for Pneumonia CXR were used. The obtained results achieved high performance with an Area Under Curve (AUC) of 0.99 for multi-class classification (COVID-19 vs. Other Pneumonia vs. normal). The sensitivity of the COVID-19 class achieved 0.99. We demonstrated that the obtained results outperformed comparable state-of-the-art models for detecting COVID-19 on CXR images using CNN architectures. The attention map for the proposed model showed that our model is able to efficiently identify the signs of COVID-19.
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Affiliation(s)
| | - Moulay A. Akhloufi
- Perception, Robotics, and Intelligent Machines Research Group (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1A 3E9, Canada;
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Chandrasekar KS. Exploring the Deep-Learning Techniques in Detecting the Presence of Coronavirus in the Chest X-Ray Images: A Comprehensive Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:5381-5395. [PMID: 35645554 PMCID: PMC9126247 DOI: 10.1007/s11831-022-09768-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 05/07/2022] [Indexed: 06/15/2023]
Abstract
The deadly coronavirus (COVID-19) is one of the dangerous diseases affecting the entire world and is fastly spreading disease. This spread can be reduced by detecting and quarantining the patients at an earlier stage. The most common diagnostic tool for detecting the coronavirus is the Reverse transcription-polymerase chain reaction (RT-PCR) test which is time-consuming and also needs more equipment and manpower. Furthermore, many countries had a deficit of RTPCR kits. This is why it is exceptionally very crucial to develop artificial intelligence (AI) techniques to detect the outbreak of coronavirus. This motivated many researchers to involve deep-learning methods using X-ray images for more decisive analysis. Thus, this paper outlines many papers that used traditional and pre-trained deep learning methods that are newly developed to reduce the spread of COVID-19 disease. Specifically, advanced deep learning methods play a critical role in extracting the features from the chest X-ray images. These features are then used to classify whether the patient is affected with coronavirus or not. Besides, this paper shows that deep learning techniques have probable applications in the medical field.
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Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A, Alsaadi FE. Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey. SN COMPUTER SCIENCE 2022; 3:286. [PMID: 35578678 PMCID: PMC9096341 DOI: 10.1007/s42979-022-01184-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 04/30/2022] [Indexed: 12/12/2022]
Abstract
The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,...) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed.
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Affiliation(s)
- Yassine Meraihi
- LIST Laboratory, University of M'Hamed Bougara Boumerdes, Avenue of Independence, 35000 Boumerdes, Algeria
| | - Asma Benmessaoud Gabis
- Ecole nationale Supérieure d'Informatique, Laboratoire des Méthodes de Conception des Systèmes, BP 68 M, 16309 Oued-Smar, Alger Algeria
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006 Australia.,Yonsei Frontier Lab, Yonsei University, Seoul, Korea
| | - Amar Ramdane-Cherif
- LISV Laboratory, University of Versailles St-Quentin-en-Yvelines, 10-12 Avenue of Europe, 78140 Velizy, France
| | - Fawaz E Alsaadi
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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Hassan F, Albahli S, Javed A, Irtaza A. A Robust Framework for Epidemic Analysis, Prediction and Detection of COVID-19. Front Public Health 2022; 10:805086. [PMID: 35602122 PMCID: PMC9120631 DOI: 10.3389/fpubh.2022.805086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Covid-19 has become a pandemic that affects lots of individuals daily, worldwide, and, particularly, the widespread disruption in numerous countries, namely, the US, Italy, India, Saudi Arabia. The timely detection of this infectious disease is mandatory to prevent the quick spread globally and locally. Moreover, the timely detection of COVID-19 in the coming time is significant to well cope with the disease control by Governments. The common symptoms of COVID are fever as well as dry cough, which is similar to the normal flu. The disease is devastating and spreads quickly, which affects individuals of all ages, particularly, aged people and those with feeble immune systems. There is a standard method employed to detect the COVID, namely, the real-time polymerase chain reaction (RT-PCR) test. But this method has shortcomings, i.e., it takes a long time and generates maximum false-positive cases. Consequently, we necessitate to propose a robust framework for the detection as well as for the estimation of COVID cases globally. To achieve the above goals, we proposed a novel technique to analyze, predict, and detect the COVID-19 infection. We made dependable estimates on significant pandemic parameters and made predictions of infection as well as potential washout time frames for numerous countries globally. We used a publicly available dataset composed by Johns Hopkins Center for estimation, analysis, and predictions of COVID cases during the time period of 21 April 2020 to 27 June 2020. We employed a simple circulation for fast as well as simple estimates of the COVID model and estimated the parameters of the Gaussian curve, utilizing a parameter, namely, the least-square parameter curve fitting for numerous countries in distinct areas. Forecasts of COVID depend upon the potential results of Gaussian time evolution with a central limit theorem of data the Covid prediction to be justified. For gaussian distribution, the parameters, namely, extreme time and thickness are regulated using a statistical Y2 fit for the aim of doubling times after 21 April 2020. Moreover, for the detection of COVID-19, we also proposed a novel technique, employing the two features, namely, Histogram of Oriented Gradients and Scale Invariant Feature Transform. We also designed a CNN-based architecture named COVIDDetectorNet for classification purposes. We fed the extracted features into the proposed COVIDDetectorNet to detect COVID-19, viral pneumonia, and other lung infections. Our method obtained an accuracy of 96.51, 92.62, and 86.53% for two, three, and four classes, respectively. Experimental outcomes illustrate that our method is reliable to be employed for the forecast and detection of COVID-19 disease.
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Affiliation(s)
- Farman Hassan
- Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan
| | - Saleh Albahli
- Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
- *Correspondence: Saleh Albahli
| | - Ali Javed
- Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan
- Department of Computer Science and Engineering, Oakland University, Detroit, MI, United States
| | - Aun Irtaza
- Department of Computer and Electrical Engineering, University of Michigan, Dearborn, MI, United States
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Gampala V, Rathan K, S CN, Shajin FH, Rajesh P. Diagnosis of COVID-19 patients by adapting hyper parametertuned deep belief network using hosted cuckoo optimization algorithm. Electromagn Biol Med 2022; 41:257-271. [PMID: 35491901 DOI: 10.1080/15368378.2022.2065679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
COVID-19 is an infection caused by recently discovered corona virus. The symptoms of COVID-19 are fever, cough and dumpiness of breathing. A quick and accurate identification is essential for an efficient fight against COVID-19. A machine learning technique is initiated for categorizing the chest x-ray images into two cases: COVID-19 positive case or negative case. In this manuscript, the categorization of COVID-19 can be determined by hyper parameter tuned deep belief network using hosted cuckoo optimization algorithm. At first, the input chest x-ray images are pre-processed for removing noises. In this manuscript, the deep belief network method is enhanced by hosted cuckoo optimization approach for getting optimum hyper tuning parameters. By this, exact categorization of COVID-19 is attained effectively. The proposed methodology is stimulated at MATLAB. The proposed approach attains 28.3% and 23.5% higher accuracy for Normal and 32.3% and 31.5% higher accuracy for COVID-19, 19.3% and 28.5% higher precision for Normal and 45.3% and 28.5% higher precision for COVID-19, 20.3% and 21.5% higher F-score for Normal and 40.3% and 21.5% higher F-score for COVID-19. The proposed methodology is analyzed using two existing methodologies, as Convolutional Neural Network with Social Mimic Optimization (CNN-SMO) and Support Vector Machine classifier using Bayesian Optimization algorithm (SVM-BOA).
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Affiliation(s)
- Veerraju Gampala
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, AP, India
| | | | - Christalin Nelson S
- Systemics Cluster, School of Computer Science, University of Petroleum and Energy Studies(UPES), Energy Acres, Bidholi, Dehradun, Uttarakhand, Dehradun, India
| | - Francis H Shajin
- Department of Electronics and Communication Engineering, Anna University, Chennai,India
| | - P Rajesh
- Department of Electrical and Electronics Engineering, Anna University, Chennai, India
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Let AI Perform Better Next Time—A Systematic Review of Medical Imaging-Based Automated Diagnosis of COVID-19: 2020–2022. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083895] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time-consuming characteristic of the Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based on X-ray images and Computed Tomography (CT) images has been widely adopted to confirm positive COVID-19 RT-PCR tests. Since the very beginning of the pandemic, researchers in the artificial intelligence area have proposed a large number of automatic diagnosing models, hoping to assist radiologists and improve the diagnosing accuracy. However, after two years of development, there are still few models that can actually be applied in real-world scenarios. Numerous problems have emerged in the research of the automated diagnosis of COVID-19. In this paper, we present a systematic review of these diagnosing models. A total of 179 proposed models are involved. First, we compare the medical image modalities (CT or X-ray) for COVID-19 diagnosis from both the clinical perspective and the artificial intelligence perspective. Then, we classify existing methods into two types—image-level diagnosis (i.e., classification-based methods) and pixel-level diagnosis (i.e., segmentation-based models). For both types of methods, we define universal model pipelines and analyze the techniques that have been applied in each step of the pipeline in detail. In addition, we also review some commonly adopted public COVID-19 datasets. More importantly, we present an in-depth discussion of the existing automated diagnosis models and note a total of three significant problems: biased model performance evaluation; inappropriate implementation details; and a low reproducibility, reliability and explainability. For each point, we give corresponding recommendations on how we can avoid making the same mistakes and let AI perform better in the next pandemic.
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Kiani S, Abasi S, Yazdani A. Evaluation of m‐Health‐rehabilitation for respiratory disorders: A systematic review. Health Sci Rep 2022; 5:e575. [PMID: 35387314 PMCID: PMC8973261 DOI: 10.1002/hsr2.575] [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: 10/23/2021] [Revised: 02/26/2022] [Accepted: 02/28/2022] [Indexed: 11/23/2022] Open
Abstract
Background and Aims Chronic respiratory diseases are prominent causes of morbidity worldwide that impose significant social and economic burdens on individuals and communities. Pulmonary rehabilitation is one of the main aspects of medical rehabilitation. Nowadays, mobile health apps deliver pulmonary rehabilitation support via smartphones. This article presents a systematic review of the literature on m‐Health apps used in respiration disorders rehabilitation. Methods A systematic search was performed on MEDLINE (through PubMed), Web of Science, and Scopus in May 2021 without any date limitation. This study was using a combination of keywords and MeSH terms associated with pulmonary rehabilitation. Relevant studies were selected by two independents and were categorized studies results. The inclusion criterion was m‐Health apps for pulmonary rehabilitation and exclusion criteria mobile‐based interventions, by voice call or short message service and cardiopulmonary articles. Results Searching scientific databases yielded 161 relevant articles. Then, 27 articles were included in the study with a complete evaluation of the articles. Sixty percent of them were related to patients with chronic obstructive pulmonary disease (COPD). Rehabilitation aiming to improve the quality of life, promote self‐management, encourage physical activity, and reduce the symptoms as the most common goals of pulmonary rehabilitation using m‐Health apps; 89% of these studies showed that m‐Health apps can be effective in improving pulmonary rehabilitation. In addition, 37% of studies reported high usability and acceptance. However, the results of some studies show that adherence to apps decreases in the long run. Conclusion Our study shows that m‐Health pulmonary rehabilitation apps are effective in improving the quality of life, self‐management, and physical activity. According to the results, it seems that using the m‐Health apps for pulmonary rehabilitation can be useful in the COVID‐19 pandemic and help reduce respiratory disorders in patients with COVID‐19 disease.
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Affiliation(s)
- Shamim Kiani
- Student Research Committee, Department of Health Information Management, School of Health Management and Information Sciences Shiraz University of Medical Sciences Shiraz Iran
| | - Sanaz Abasi
- Student Research Committee, Department of Health Information Management, School of Health Management and Information Sciences Shiraz University of Medical Sciences Shiraz Iran
| | - Azita Yazdani
- Department of Health Information Management, Clinical Education Research Center, Health Human Resources Research Center, School of Health Management and Information Sciences Shiraz University of Medical Sciences Shiraz Iran
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End-to-End Multi-Task Learning Approaches for the Joint Epiretinal Membrane Segmentation and Screening in OCT Images. Comput Med Imaging Graph 2022; 98:102068. [DOI: 10.1016/j.compmedimag.2022.102068] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/28/2022] [Accepted: 04/18/2022] [Indexed: 02/07/2023]
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Mary Shyni H, Chitra E. A COMPARATIVE STUDY OF X-RAY AND CT IMAGES IN COVID-19 DETECTION USING IMAGE PROCESSING AND DEEP LEARNING TECHNIQUES. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE UPDATE 2022; 2:100054. [PMID: 35281724 PMCID: PMC8898857 DOI: 10.1016/j.cmpbup.2022.100054] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The deadly coronavirus has not just devastated the lives of millions but has put the entire healthcare system under tremendous pressure. Early diagnosis of COVID-19 plays a significant role in isolating the positive cases and preventing the further spread of the disease. The medical images along with deep learning models provided faster and more accurate results in the detection of COVID-19. This article extensively reviews the recent deep learning techniques for COVID-19 diagnosis. The research articles discussed reveal that Convolutional Neural Network (CNN) is the most popular deep learning algorithm in detecting COVID-19 from medical images. An overview of the necessity of pre-processing the medical images, transfer learning and data augmentation techniques to deal with data scarcity problems, use of pre-trained models to save time and the role of medical images in the automatic detection of COVID-19 are summarized. This article also provides a sensible outlook for the young researchers to develop highly effective CNN models coupled with medical images in the early detection of the disease.
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Affiliation(s)
- H Mary Shyni
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India
| | - E Chitra
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India
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Nayak J, Naik B, Dinesh P, Vakula K, Dash PB, Pelusi D. Significance of deep learning for Covid-19: state-of-the-art review. RESEARCH ON BIOMEDICAL ENGINEERING 2022. [PMCID: PMC7980106 DOI: 10.1007/s42600-021-00135-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Purpose The appearance of the 2019 novel coronavirus (Covid-19), for which there is no treatment or a vaccine, formed a sense of necessity for new drug discovery advances. The pandemic of NCOV-19 (novel coronavirus-19) has been engaged as a public health disaster of overall distress by the World Health Organization. Different pandemic models for NCOV-19 are being exploited by researchers all over the world to acquire experienced assessments and impose major control measures. Among the standard techniques for NCOV-19 global outbreak prediction, epidemiological and simple statistical techniques have attained more concern by researchers. Insufficiency and deficiency of health tests for identifying a solution became a major difficulty in controlling the spread of NCOV-19. To solve this problem, deep learning has emerged as a novel solution over a dozen of machine learning techniques. Deep learning has attained advanced performance in medical applications. Deep learning has the capacity of recognizing patterns in large complex datasets. They are identified as an appropriate method for analyzing affected patients of NCOV-19. Conversely, these techniques for disease recognition focus entirely on enhancing the accurateness of forecasts or classifications without the ambiguity measure in a decision. Knowing how much assurance present in a computer-based health analysis is necessary for gaining clinicians’ expectations in the technology and progress treatment consequently. Today, NCOV-19 diseases are the main healthcare confront throughout the world. Detecting NCOV-19 in X-ray images is vital for diagnosis, treatment, and evaluation. Still, analytical ambiguity in a report is a difficult yet predictable task for radiologists. Method In this paper, an in-depth analysis has been performed on the significance of deep learning for Covid-19 and as per the standard search database, this is the first review research work ever made concentrating particularly on Deep Learning for NCOV-19. Conclusion The main aim behind this research work is to inspire the research community and to innovate novel research using deep learning. Moreover, the outcome of this detailed structured review on the impact of deep learning in covid-19 analysis will be helpful for further investigations on various modalities of diseases detection, prevention and finding novel solutions.
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Affiliation(s)
- Janmenjoy Nayak
- Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), K Kotturu, Tekkali, AP 532201 India
| | - Bighnaraj Naik
- Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Odisha 768018 India
| | - Paidi Dinesh
- Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, AP 532402 India
| | - Kanithi Vakula
- Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, AP 532402 India
| | - Pandit Byomakesha Dash
- Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Odisha 768018 India
| | - Danilo Pelusi
- Faculty of Communication Sciences, University of Teramo, Coste Sant', Agostino Campus, Teramo, Italy
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