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Karthick S, Gomathi N. IoT-based COVID-19 detection using recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm. Med Biol Eng Comput 2024; 62:925-940. [PMID: 38095786 DOI: 10.1007/s11517-023-02973-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 11/15/2023] [Indexed: 02/22/2024]
Abstract
New potential for healthcare has been made possible by the development of the Internet of Medical Things (IoMT) with deep learning. This is applied for a broad range of applications. Normal medical devices together with sensors can gather important data when connected to the Internet, and deep learning uses this data to reveal symptoms and patterns and activate remote care. In recent years, the COVID-19 pandemic caused more mortality. Millions of people have been affected by this virus, and the number of infections is continually rising daily. To detect COVID-19, researchers attempt to utilize medical imaging and deep learning-based methods. Several methodologies were suggested utilizing chest X-ray (CXR) images for COVID-19 diagnosis. But these methodologies do not provide satisfactory accuracy. To overcome these drawbacks, a recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm (RERNN-GEO) is proposed in this paper. The intention of this work is to provide IoT-based deep learning method for the premature identification of COVID-19. This paradigm can be able to ease the workload of radiologists and medical specialists and also help with pandemic control. RERNN-GEO is a deep learning-based method; this is utilized in chest X-ray (CXR) images for COVID-19 diagnosis. Here, the Gray-Level Co-Occurrence Matrix (GLCM) window adaptive algorithm is used for extracting features to enable accurate diagnosis. By utilizing this algorithm, the proposed method attains better accuracy (33.84%, 28.93%, and 33.03%) and lower execution time (11.06%, 33.26%, and 23.33%) compared with the existing methods. This method can be capable of helping the clinician/radiologist to validate the initial assessment related to COVID-19.
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Affiliation(s)
- Karthick S
- Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi - NCR Campus, Ghaziabad, India.
| | - Gomathi N
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, 600062, India
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2
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Nahiduzzaman M, Faruq Goni MO, Robiul Islam M, Sayeed A, Shamim Anower M, Ahsan M, Haider J, Kowalski M. Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture. Biocybern Biomed Eng 2023; 43:S0208-5216(23)00037-2. [PMID: 38620111 PMCID: PMC10292668 DOI: 10.1016/j.bbe.2023.06.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 04/04/2023] [Accepted: 06/16/2023] [Indexed: 11/09/2023]
Abstract
Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly and medically vulnerable patients. In the last few decades, several new types of lung-related diseases have taken the lives of millions of people, and COVID-19 has taken almost 6.27 million lives. To fight against lung diseases, timely and correct diagnosis with appropriate treatment is crucial in the current COVID-19 pandemic. In this study, an intelligent recognition system for seven lung diseases has been proposed based on machine learning (ML) techniques to aid the medical experts. Chest X-ray (CXR) images of lung diseases were collected from several publicly available databases. A lightweight convolutional neural network (CNN) has been used to extract characteristic features from the raw pixel values of the CXR images. The best feature subset has been identified using the Pearson Correlation Coefficient (PCC). Finally, the extreme learning machine (ELM) has been used to perform the classification task to assist faster learning and reduced computational complexity. The proposed CNN-PCC-ELM model achieved an accuracy of 96.22% with an Area Under Curve (AUC) of 99.48% for eight class classification. The outcomes from the proposed model demonstrated better performance than the existing state-of-the-art (SOTA) models in the case of COVID-19, pneumonia, and tuberculosis detection in both binary and multiclass classifications. For eight class classification, the proposed model achieved precision, recall and fi-score and ROC are 100%, 99%, 100% and 99.99% respectively for COVID-19 detection demonstrating its robustness. Therefore, the proposed model has overshadowed the existing pioneering models to accurately differentiate COVID-19 from the other lung diseases that can assist the medical physicians in treating the patient effectively.
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Affiliation(s)
- Md Nahiduzzaman
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Omaer Faruq Goni
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Robiul Islam
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Abu Sayeed
- Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Shamim Anower
- Department of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Mominul Ahsan
- Department of Computer Science, University of York, Deramore Lane, Heslington, York YO10 5GH, UK
| | - Julfikar Haider
- Department of Engineering, Manchester Metropolitan University, Chester St, Manchester M1 5GD, UK
| | - Marcin Kowalski
- Institute of Optoelectronics, Military University of Technology, Gen. S. Kaliskiego 2, Warsaw, Poland
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Gupta A, Mishra S, Sahu SC, Srinivasarao U, Naik KJ. Application of Convolutional Neural Networks for COVID-19 Detection in X-ray Images Using InceptionV3 and U-Net. NEW GENERATION COMPUTING 2023; 41:475-502. [PMID: 37229179 PMCID: PMC10173914 DOI: 10.1007/s00354-023-00217-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 04/25/2023] [Indexed: 05/27/2023]
Abstract
COVID-19 has expanded overall across the globe after its initial cases were discovered in December 2019 in Wuhan-China. Because the virus has impacted people's health worldwide, its fast identification is essential for preventing disease spread and reducing mortality rates. The reverse transcription polymerase chain reaction (RT-PCR) is the primary leading method for detecting COVID-19 disease; it has high costs and long turnaround times. Hence, quick and easy-to-use innovative diagnostic instruments are required. According to a new study, COVID-19 is linked to discoveries in chest X-ray pictures. The suggested approach includes a stage of pre-processing with lung segmentation, removing the surroundings that do not provide information pertinent to the task and may result in biased results. The InceptionV3 and U-Net deep learning models used in this work process the X-ray photo and classifies them as COVID-19 negative or positive. The CNN model that uses a transfer learning approach was trained. Finally, the findings are analyzed and interpreted through different examples. The obtained COVID-19 detection accuracy is around 99% for the best models.
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Affiliation(s)
- Aman Gupta
- Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur , Chhattisgarh India
| | - Shashank Mishra
- Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur , Chhattisgarh India
| | - Sourav Chandan Sahu
- Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur , Chhattisgarh India
| | - Ulligaddala Srinivasarao
- Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur , Chhattisgarh India
| | - K. Jairam Naik
- Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur , Chhattisgarh India
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Agrawal T, Choudhary P. COVID-SegNet: encoder-decoder-based architecture for COVID-19 lesion segmentation in chest X-ray. MULTIMEDIA SYSTEMS 2023; 29:1-14. [PMID: 37360154 PMCID: PMC10115388 DOI: 10.1007/s00530-023-01096-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 04/10/2023] [Indexed: 06/28/2023]
Abstract
The coronavirus disease 2019, initially named 2019-nCOV (COVID-19) has been declared a global pandemic by the World Health Organization in March 2020. Because of the growing number of COVID patients, the world's health infrastructure has collapsed, and computer-aided diagnosis has become a necessity. Most of the models proposed for the COVID-19 detection in chest X-rays do image-level analysis. These models do not identify the infected region in the images for an accurate and precise diagnosis. The lesion segmentation will help the medical experts to identify the infected region in the lungs. Therefore, in this paper, a UNet-based encoder-decoder architecture is proposed for the COVID-19 lesion segmentation in chest X-rays. To improve performance, the proposed model employs an attention mechanism and a convolution-based atrous spatial pyramid pooling module. The proposed model obtained 0.8325 and 0.7132 values of the dice similarity coefficient and jaccard index, respectively, and outperformed the state-of-the-art UNet model. An ablation study has been performed to highlight the contribution of the attention mechanism and small dilation rates in the atrous spatial pyramid pooling module.
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Affiliation(s)
- Tarun Agrawal
- Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh 177005 India
| | - Prakash Choudhary
- Department of Computer Science and Engineering, Central University of Rajasthan, Ajmer, Rajasthan India
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Agrawal T, Choudhary P. ReSE‐Net: Enhanced UNet architecture for lung segmentation in chest radiography images. Comput Intell 2023. [DOI: 10.1111/coin.12575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Affiliation(s)
- Tarun Agrawal
- Department of Computer Science and Engineering NIT Hamirpur Hamirpur Himachal Pradesh India
| | - Prakash Choudhary
- Department of Computer Science and Engineering Central University of Rajasthan Ajmer Rajasthan India
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Xue X, Chinnaperumal S, Abdulsahib GM, Manyam RR, Marappan R, Raju SK, Khalaf OI. Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets. Bioengineering (Basel) 2023; 10:363. [PMID: 36978754 PMCID: PMC10045423 DOI: 10.3390/bioengineering10030363] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023] Open
Abstract
Recently, various methods have been developed to identify COVID-19 cases, such as PCR testing and non-contact procedures such as chest X-rays and computed tomography (CT) scans. Deep learning (DL) and artificial intelligence (AI) are critical tools for early and accurate detection of COVID-19. This research explores the different DL techniques for identifying COVID-19 and pneumonia on medical CT and radiography images using ResNet152, VGG16, ResNet50, and DenseNet121. The ResNet framework uses CT scan images with accuracy and precision. This research automates optimum model architecture and training parameters. Transfer learning approaches are also employed to solve content gaps and shorten training duration. An upgraded VGG16 deep transfer learning architecture is applied to perform multi-class classification for X-ray imaging tasks. Enhanced VGG16 has been proven to recognize three types of radiographic images with 99% accuracy, typical for COVID-19 and pneumonia. The validity and performance metrics of the proposed model were validated using publicly available X-ray and CT scan data sets. The suggested model outperforms competing approaches in diagnosing COVID-19 and pneumonia. The primary outcomes of this research result in an average F-score (95%, 97%). In the event of healthy viral infections, this research is more efficient than existing methodologies for coronavirus detection. The created model is appropriate for recognition and classification pre-training. The suggested model outperforms traditional strategies for multi-class categorization of various illnesses.
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Affiliation(s)
- Xingsi Xue
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350011, China
| | - Seelammal Chinnaperumal
- Department of Computer Science and Engineering, Solamalai College of Engineering, Madurai 625020, Tamil Nadu, India
| | | | - Rajasekhar Reddy Manyam
- Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati Campus, Mangalagiri 522503, Andhra Pradesh, India
| | - Raja Marappan
- School of Computing, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India
| | - Sekar Kidambi Raju
- School of Computing, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India
| | - Osamah Ibrahim Khalaf
- Department of Solar, Al-Nahrain Renewable Energy Research Center, Al-Nahrain University, Baghdad 64040, Iraq
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Tuncer I, Barua PD, Dogan S, Baygin M, Tuncer T, Tan RS, Yeong CH, Acharya UR. Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography. INFORMATICS IN MEDICINE UNLOCKED 2022; 36:101158. [PMID: 36618887 PMCID: PMC9804964 DOI: 10.1016/j.imu.2022.101158] [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: 12/13/2022] [Revised: 12/30/2022] [Accepted: 12/30/2022] [Indexed: 01/01/2023] Open
Abstract
Background Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering. Material and method We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm. Results Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity. Conclusions Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that our presented swin-textural is a self-organized, highly accurate, and lightweight image classification model and is better than the compared deep learning models for this dataset.
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Affiliation(s)
- Ilknur Tuncer
- Elazig Governorship, Interior Ministry, Elazig, Turkey
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, QLD, 4350, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Mehmet Baygin
- Department of Computer Engineering, Faculty of Engineering, Ardahan University, Ardahan, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore
- Duke-NUS Medical School, Singapore
| | - Chai Hong Yeong
- School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500, Subang Jaya, Malaysia
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Lasker A, Obaidullah SM, Chakraborty C, Roy K. Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review. SN COMPUTER SCIENCE 2022; 4:65. [PMID: 36467853 PMCID: PMC9702883 DOI: 10.1007/s42979-022-01464-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 10/18/2022] [Indexed: 11/26/2022]
Abstract
Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.
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Affiliation(s)
- Asifuzzaman Lasker
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Sk Md Obaidullah
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Chandan Chakraborty
- Department of Computer Science & Engineering, National Institute of Technical Teachers’ Training & Research Kolkata, Kolkata, India
| | - Kaushik Roy
- Department of Computer Science, West Bengal State University, Barasat, India
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Ghose P, Uddin MA, Acharjee UK, Sharmin S. Deep viewing for the identification of Covid-19 infection status from chest X-Ray image using CNN based architecture. INTELLIGENT SYSTEMS WITH APPLICATIONS 2022; 16. [PMCID: PMC9536212 DOI: 10.1016/j.iswa.2022.200130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In recent years, coronavirus (Covid-19) has evolved into one of the world’s leading life-threatening severe viral illnesses. A self-executing accord system might be a better option to stop Covid-19 from spreading due to its quick diagnostic option. Many researches have already investigated various deep learning techniques, which have a significant impact on the quick and precise early detection of Covid-19. Most of the existing techniques, though, have not been trained and tested using a significant amount of data. In this paper, we purpose a deep learning technique enabled Convolutional Neural Network (CNN) to automatically diagnose Covid-19 from chest x-rays. To train and test our model, 10,293 x-rays, including 2875 x-rays of Covid-19, were collected as a data set. The applied dataset consists of three groups of chest x-rays: Covid-19, pneumonia, and normal patients. The proposed approach achieved 98.5% accuracy, 98.9% specificity, 99.2% sensitivity, 99.2% precision, and 98.3% F1-score. Distinguishing Covid-19 patients from pneumonia patients using chest x-ray, particularly for human eyes is crucial since both diseases have nearly identical characteristics. To address this issue, we have categorized Covid-19 and pneumonia using x-rays, achieving a 99.60% accuracy rate. Our findings show that the proposed model might aid clinicians and researchers in rapidly detecting Covid-19 patients, hence facilitating the treatment of Covid-19 patients.
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Affiliation(s)
- Partho Ghose
- Depaprtment of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh,Corresponding author
| | - Md. Ashraf Uddin
- Depaprtment of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Uzzal Kumar Acharjee
- Depaprtment of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Selina Sharmin
- Depaprtment of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
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Addo D, Zhou S, Jackson JK, Nneji GU, Monday HN, Sarpong K, Patamia RA, Ekong F, Owusu-Agyei CA. EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images. Diagnostics (Basel) 2022; 12:2569. [PMID: 36359413 PMCID: PMC9689048 DOI: 10.3390/diagnostics12112569] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/13/2022] [Accepted: 10/18/2022] [Indexed: 09/08/2024] Open
Abstract
The COVID-19 pandemic has had a significant impact on many lives and the economies of many countries since late December 2019. Early detection with high accuracy is essential to help break the chain of transmission. Several radiological methodologies, such as CT scan and chest X-ray, have been employed in diagnosing and monitoring COVID-19 disease. Still, these methodologies are time-consuming and require trial and error. Machine learning techniques are currently being applied by several studies to deal with COVID-19. This study exploits the latent embeddings of variational autoencoders combined with ensemble techniques to propose three effective EVAE-Net models to detect COVID-19 disease. Two encoders are trained on chest X-ray images to generate two feature maps. The feature maps are concatenated and passed to either a combined or individual reparameterization phase to generate latent embeddings by sampling from a distribution. The latent embeddings are concatenated and passed to a classification head for classification. The COVID-19 Radiography Dataset from Kaggle is the source of chest X-ray images. The performances of the three models are evaluated. The proposed model shows satisfactory performance, with the best model achieving 99.19% and 98.66% accuracy on four classes and three classes, respectively.
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Affiliation(s)
- Daniel Addo
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Shijie Zhou
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Jehoiada Kofi Jackson
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Grace Ugochi Nneji
- Department of Computing, Oxford Brookes College of Chengdu University of Technology, Chengdu 610059, China
| | - Happy Nkanta Monday
- Department of Computing, Oxford Brookes College of Chengdu University of Technology, Chengdu 610059, China
| | - Kwabena Sarpong
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Rutherford Agbeshi Patamia
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Favour Ekong
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
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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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13
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Alathari MJA, Al Mashhadany Y, Mokhtar MHH, Burham N, Bin Zan MSD, A Bakar AA, Arsad N. Human Body Performance with COVID-19 Affectation According to Virus Specification Based on Biosensor Techniques. SENSORS (BASEL, SWITZERLAND) 2021; 21:8362. [PMID: 34960456 PMCID: PMC8704003 DOI: 10.3390/s21248362] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 12/12/2022]
Abstract
Life was once normal before the first announcement of COVID-19's first case in Wuhan, China, and what was slowly spreading became an overnight worldwide pandemic. Ever since the virus spread at the end of 2019, it has been morphing and rapidly adapting to human nature changes which cause difficult conundrums in the efforts of fighting it. Thus, researchers were steered to investigate the virus in order to contain the outbreak considering its novelty and there being no known cure. In contribution to that, this paper extensively reviewed, compared, and analyzed two main points; SARS-CoV-2 virus transmission in humans and detection methods of COVID-19 in the human body. SARS-CoV-2 human exchange transmission methods reviewed four modes of transmission which are Respiratory Transmission, Fecal-Oral Transmission, Ocular transmission, and Vertical Transmission. The latter point particularly sheds light on the latest discoveries and advancements in the aim of COVID-19 diagnosis and detection of SARS-CoV-2 virus associated with this disease in the human body. The methods in this review paper were classified into two categories which are RNA-based detection including RT-PCR, LAMP, CRISPR, and NGS and secondly, biosensors detection including, electrochemical biosensors, electronic biosensors, piezoelectric biosensors, and optical biosensors.
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Affiliation(s)
- Mohammed Jawad Ahmed Alathari
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia; (M.J.A.A.); (M.H.H.M.); (N.B.); (M.S.D.B.Z.); (A.A.A.B.)
| | - Yousif Al Mashhadany
- Department of Electrical Engineering, College of Engineering, University of Anbar, Anbar 00964, Iraq;
| | - Mohd Hadri Hafiz Mokhtar
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia; (M.J.A.A.); (M.H.H.M.); (N.B.); (M.S.D.B.Z.); (A.A.A.B.)
| | - Norhafizah Burham
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia; (M.J.A.A.); (M.H.H.M.); (N.B.); (M.S.D.B.Z.); (A.A.A.B.)
- School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Shah Alam 40450, Malaysia
| | - Mohd Saiful Dzulkefly Bin Zan
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia; (M.J.A.A.); (M.H.H.M.); (N.B.); (M.S.D.B.Z.); (A.A.A.B.)
| | - Ahmad Ashrif A Bakar
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia; (M.J.A.A.); (M.H.H.M.); (N.B.); (M.S.D.B.Z.); (A.A.A.B.)
| | - Norhana Arsad
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia; (M.J.A.A.); (M.H.H.M.); (N.B.); (M.S.D.B.Z.); (A.A.A.B.)
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14
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Gudigar A, Raghavendra U, Nayak S, Ooi CP, Chan WY, Gangavarapu MR, Dharmik C, Samanth J, Kadri NA, Hasikin K, Barua PD, Chakraborty S, Ciaccio EJ, Acharya UR. Role of Artificial Intelligence in COVID-19 Detection. SENSORS (BASEL, SWITZERLAND) 2021; 21:8045. [PMID: 34884045 PMCID: PMC8659534 DOI: 10.3390/s21238045] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 11/26/2021] [Accepted: 11/26/2021] [Indexed: 12/15/2022]
Abstract
The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Sneha Nayak
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia;
| | - Mokshagna Rohit Gangavarapu
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chinmay Dharmik
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia;
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Subrata Chakraborty
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
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