Automatic covid-19 detection using exemplar hybrid deep features with x-ray images
Article
Barua, Prabal Datta, Muhammad Gowdh, Nadia Fareeda, Rahmat, Kartini, Ramli, Norlisah, Ng, Wei Lin, Chan, Wai Yee, Kuluozturk, Mutlu, Dogan, Sengul, Baygin, Mehmet, Yaman, Orhan, Tuncer, Turker, Wen, Tao, Cheong, Kang Hao and Acharya, U. Rajendra. 2021. "Automatic covid-19 detection using exemplar hybrid deep features with x-ray images." International Journal of Environmental Research and Public Health. 18 (15). https://doi.org/10.3390/ijerph18158052
Article Title | Automatic covid-19 detection using exemplar hybrid deep features with x-ray images |
---|---|
ERA Journal ID | 44293 |
Article Category | Article |
Authors | Barua, Prabal Datta, Muhammad Gowdh, Nadia Fareeda, Rahmat, Kartini, Ramli, Norlisah, Ng, Wei Lin, Chan, Wai Yee, Kuluozturk, Mutlu, Dogan, Sengul, Baygin, Mehmet, Yaman, Orhan, Tuncer, Turker, Wen, Tao, Cheong, Kang Hao and Acharya, U. Rajendra |
Journal Title | International Journal of Environmental Research and Public Health |
Journal Citation | 18 (15) |
Article Number | 8052 |
Number of Pages | 20 |
Year | 2021 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1660-4601 |
1661-7827 | |
Digital Object Identifier (DOI) | https://doi.org/10.3390/ijerph18158052 |
Web Address (URL) | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111290850&doi=10.3390%2fijerph18158052&partnerID=40&md5=c35d562da2bb6cf007b64f73bf76538f |
Abstract | ray |
COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. | |
Keywords | COVID-19 detection; Exemplar COVID-19FclNet9; deep feature generation; transfer learning; iterative NCA |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4206. Public health |
Byline Affiliations | School of Management and Enterprise |
University of Malaya, Malaysia | |
Firat University Hospital, Turkey | |
Firat University, Turkey | |
Ardahan University, Turkiye | |
Singapore University of Technology and Design | |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
Permalink -
https://research.usq.edu.au/item/z1v67/automatic-covid-19-detection-using-exemplar-hybrid-deep-features-with-x-ray-images
Download files
72
total views18
total downloads4
views this month0
downloads this month
Export as
Related outputs
Automated hip dysplasia detection using novel FlexiLBPHOG model with ultrasound images
Key, Sefa, Kurum, Huseyin, Esmez, Omer, Hafeez-Baig, Abdul, Hajiyeva, Rena, Dogan, Sengul and Tuncer, Turker. 2025. "Automated hip dysplasia detection using novel FlexiLBPHOG model with ultrasound images." Ain Shams Engineering Journal. 16 (1). https://doi.org/10.1016/j.asej.2024.103235Artificial Intelligence-Based Suicide Prevention and Prediction: A Systematic Review (2019-2023)
Atmakuru, Anirudh, Shahini, Alen, Chakraborty, Subrata, Seoni, Silvia, Salvi, Massimo, Hafeez-Baig, Abdul, Rashid, Sadaf, Tan, Ru San, Barua, Prabal Datta, Molinari, Filippo and Acharya, U Rajendra. 2025. "Artificial Intelligence-Based Suicide Prevention and Prediction: A Systematic Review (2019-2023)." Information Fusion. 114. https://doi.org/10.1016/j.inffus.2024.102673Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts
Ghimire, Sujan, AL-Musaylh, Mohanad S., Nguyen-Huy, Thong, Deo, Ravinesh C., Acharya, Rajendra, Casillas-Perez, David, Yaseen, Zaher Mundher and Salcedo-sanz, Sancho. 2025. "Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts." Applied Energy. 378 (Part A). https://doi.org/10.1016/j.apenergy.2024.124763AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models
Aydin, Mehmet, Barua, Prabal Datta, Chadalavada, Sreenivasulu, Dogan, Sengul, Tuncer, Turker, Chakraborty, Subrata and Acharya, Rajendra U.. 2025. "AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models." Multimedia Tools and Applications. 84 (4), pp. 1821-1843. https://doi.org/10.1007/s11042-024-19163-2Directed Lobish-based explainable feature engineering model with TTPat and CWINCA for EEG artifact classification
Tuncer, Turker, Dogan, Sengul, Baygin, Mehmet, Tasci, Irem, Mungen, Bulent, Tasci, Burak, Barua, Prabal Datta and Acharya, U.R.. 2024. "Directed Lobish-based explainable feature engineering model with TTPat and CWINCA for EEG artifact classification." Knowledge-Based Systems. 305. https://doi.org/10.1016/j.knosys.2024.112555Retinal Health Screening Using Artificial Intelligence with Digital Fundus Images: A Review of the Last Decade (2012-2023)
Islam, Saad, Deo, Ravinesh C., Barua, Prabal Datta, Soar, Jeffrey, Yu, Ping and Acharya, U. Rajendra. 2024. "Retinal Health Screening Using Artificial Intelligence with Digital Fundus Images: A Review of the Last Decade (2012-2023)." IEEE Access. 12, pp. 176630-176685. https://doi.org/10.1109/ACCESS.2024.3477420Automated EEG-based language detection using directed quantum pattern technique
Dogan, Sengul, Tuncer, Turker, Barua, Prabal Datta and Acharya, U.R.. 2024. "Automated EEG-based language detection using directed quantum pattern technique." Applied Soft Computing. 167 (Part A). https://doi.org/10.1016/j.asoc.2024.112301A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images
Katar, Oguzhan, Yildirim, Ozal, Tan, Ru-San and Acharya, U Rajendra. 2024. "A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images." Diagnostics. 14 (22). https://doi.org/10.3390/diagnostics14222497Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies
Akpinar, Muhammed Halil, Sengur, Abdulkadir, Salvi, Massimo, Seoni, Silvia, Faust, Oliver, Mir, Hasan, Molinari,Filippo and Acharya, U. Rajendra. 2024. "Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies." IEEE Open Journal of Engineering in Medicine and Biology. 6, pp. 183-192. https://doi.org/10.1109/OJEMB.2024.3508472RECOMED: A comprehensive pharmaceutical recommendation system
Zomorodi, Mariam, Ghodsollahee, Ismail, Martin, Jennifer H, Talley, Nicholas J, Salari, Vahid, Pławiak, Paweł, Rahimi, Kazem and Acharya, U.R.. 2024. "RECOMED: A comprehensive pharmaceutical recommendation system." Artificial Intelligence in Medicine. 157. https://doi.org/10.1016/j.artmed.2024.102981Artificial intelligence in assessing cardiovascular diseases and risk factors via retinal fundus images: A review of the last decade
Abdollahi, Mirsaeed, Jafarizadeh, Ali, Ghafouri-Asbagh, Amirhosein, Sobhi, Navid, Pourmoghtader, Keysan, Pedrammehr, Siamak, Asadi, Houshyar, Tan, Ru-San, Alizadehsani, Roohallah and Acharya, U. Rajendra. 2024. "Artificial intelligence in assessing cardiovascular diseases and risk factors via retinal fundus images: A review of the last decade." WIREs Data Mining and Knowledge Discovery. 14 (6). https://doi.org/10.1002/widm.1560Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert–Huang and wavelet transforms with explainable vision transformer and CNN models
Telangore, Hardik, Azad, Victor, Sharma, Manish, Bhurane, Ankit, Tan, Ru San and Acharya, U. Rajendra. 2024. "Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert–Huang and wavelet transforms with explainable vision transformer and CNN models." Computer Methods and Programs in Biomedicine. 257. https://doi.org/10.1016/j.cmpb.2024.108455A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization
Hardalac, Firat, Akmal, Haad, Ayturan, Kubilay, Acharya, U. Rajendra and Tan, Ru-San. 2024. "A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization." Interdisciplinary Sciences: Computational Life Sciences. 16 (4), pp. 882-906. https://doi.org/10.1007/s12539-024-00647-6Automated System for the Detection of Heart Anomalies Using Phonocardiograms: A Systematic Review
Gudigar, Anjan, Raghavendra, U., Maithri, M., Samanth, Jyothi, Inamdar, Mahesh Anil, Vidhya, V., Vicnesh, Jahmunah, Prabhu, Mukund A., Tan, Ru-San, Yeong, Chai Hong, Molinari, Filippo and Acharya, U. R.. 2024. "Automated System for the Detection of Heart Anomalies Using Phonocardiograms: A Systematic Review." IEEE Access. 12, pp. 138399-138428. https://doi.org/10.1109/ACCESS.2024.3465511