A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization
Open accessAbstract
A pneumonia of unknown causes, which was detected in Wuhan, China, and spread rapidly throughout the world, was declared as Coronavirus disease 2019 (COVID-19). Thousands of people have lost their lives to this disease. Its negative effects on public health are ongoing. In this study, an intelligence computer-aided model that can automatically detect positive COVID-19 cases is proposed to support daily clinical applications. The proposed model is based on the convolution neural network (CNN) architecture and can automatically reveal discriminative features on chest X-ray images through its convolution with rich filter families, abstraction, and weight-sharing characteristics. Contrary to the generally used transfer learning approach, the proposed deep CNN model was trained from scratch. Instead of the pre-trained CNNs, a novel serial network consisting of five convolution layers was designed. This CNN model was utilized as a deep feature extractor. The extracted deep discriminative features were used to feed the machine learning algorithms, which were k-nearest neighbor, support vector machine (SVM), and decision tree. The hyperparameters of the machine learning models were optimized using the Bayesian optimization algorithm. The experiments were conducted on a public COVID-19 radiology database. The database was divided into two parts as training and test sets with 70% and 30% rates, respectively. As a result, the most efficient results were ensured by the SVM classifier with an accuracy of 98.97%, a sensitivity of 89.39%, a specificity of 99.75%, and an F-score of 96.72%. Consequently, a cheap, fast, and reliable intelligence tool has been provided for COVID-19 infection detection. The developed model can be used to assist field specialists, physicians, and radiologists in the decision-making process. Thanks to the proposed tool, the misdiagnosis rates can be reduced, and the proposed model can be used as a retrospective evaluation tool to validate positive COVID-19 infection cases.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
CNN has become the primary tool for automatic COVID-19 diagnosis in many hospitals around the world using chest X-ray images.
“CNN has become the primary tool for automatic COVID-19 diagnosis in many hospitals around the world using chest X-ray images [256–260].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [259]
The Discussion (page unavailable) says many CNN-based models using chest X-rays have been proposed for COVID-19 evaluation. The Abstract and Conclusion describe this paper’s model as a proposed aid to clinical practice, tested on a public radiology database. They do not establish that CNNs had become the primary diagnostic tool in hospitals worldwide. (LLM, conf=0.87)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415 · see citing works
Instance-Based Learning Algorithms
Aha, D. W., Kibler, D. F., & Albert, M. K. (1991) · Machine Learning · doi:10.1023/a:1022689900470 · Cited by 5,217 · see citing works
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842 · see citing works
A survey of decision tree classifier methodology
Safavian, S. R., & Landgrebe, D. A. (1991) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.97458 · Cited by 3,805 · see citing works
Wang, L., Lin, Z. Q., & Wong, A. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-76550-z · Cited by 3,151 · see citing works
Automated detection of COVID-19 cases using deep neural networks with X-ray images
Öztürk, T., Talo, M., Yildirim, E. A., Baloğlu, U. B., Yıldırım, Ö., & Acharya, U. R. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103792 · Cited by 2,607 · see citing works
Apostolopoulos, I. D., & Mpesiana, T. A. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00865-4 · Cited by 2,338 · see citing works
Narin, A., Kaya, C., & Pamuk, Z. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-00984-y · Cited by 2,054 · see citing works
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07715-1 · Cited by 1,668 · see citing works
Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns
Al-Awadhi, A. M., Alsaifi, K., Al-Awadhi, A. M., & Alhammadi, S. (2020) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2020.100326 · Cited by 1,436 · see citing works
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT
Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., & Wang, X. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2995965 · Cited by 1,237 · see citing works
COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray ImagesPreprint
Hemdan, E. E., Shouman, M. A., & Karar, M. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11055 · Cited by 1,181 · see citing works
A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)Preprint
Wang, S., Kang, B., Ma, J., et al. (2020) · medRxiv · doi:10.1101/2020.02.14.20023028 · Cited by 1,035 · see citing works
Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images
Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Zhao, H., Wang, R., Chong, Y., et al. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3065361 · Cited by 960 · see citing works
Prokop, M., van Everdingen, W. M., Vellinga, T. V. R., van Ufford, H. M. E. Q., Stöger, J. L., Beenen, L. F. M., Geurts, B. H. J., Gietema, H. A., Krdzalic, J., Schaefer‐Prokop, C. M., et al. (2020) · Radiology · doi:10.1148/radiol.2020201473 · Cited by 922 · see citing works
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets
Oh, Y., Park, S. J., & Ye, J. C. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2993291 · Cited by 897 · see citing works
Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & Mohammadi, A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103795 · Cited by 848 · see citing works
Uçar, F., & Korkmaz, D. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2020.109761 · Cited by 768 · see citing works
Essentials for Radiologists on COVID-19: An Update—Radiology Scientific Expert Panel
Kanne, J. P., Little, B. P., Chung, J. H., Elicker, B. M., & Ketai, L. H. (2020) · Radiology · doi:10.1148/radiol.2020200527 · Cited by 727 · see citing works
Detection of Coronavirus Disease (COVID-19) Based on Deep FeaturesPreprint
Sethy, P. K., & Behera, S. K. (2020) · Preprints.org · doi:10.20944/preprints202003.0300.v1 · Cited by 723 · see citing works
COVID-19 Image Data CollectionPreprint
Cohen, J., Morrison, P., & Dao, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11597 · Cited by 722 · see citing works
CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection
Waheed, A., Goyal, M., Gupta, D., Khanna, A., Al-turjman, F., & Pinheiro, P. (2020) · IEEE Access · doi:10.1109/access.2020.2994762 · Cited by 659 · see citing works
Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet
Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menéndez, R., & Singh, V. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109944 · Cited by 645 · see citing works
Estimation of COVID-19 prevalence in Italy, Spain, and France
Ceylan, Z. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.138817 · Cited by 593 · see citing works
Toğaçar, M., Ergen, B., & Cömert, Z. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103805 · Cited by 574 · see citing works
Self-citationSupport Vector Machines for Classification
Awad, M., & Khanna, R. (2015) · Apress eBooks · doi:10.1007/978-1-4302-5990-9_3 · Cited by 547 · see citing works
AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system
Wang, B., Jin, S., Yan, Q., Xu, H., Luo, C. W., Wei, L., Zhao, W. Z., Hou, X., Ma, W., Xu, Z., et al. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106897 · Cited by 539 · see citing works
Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing
Tuli, S., Tuli, S., Tuli, R., & Gill, S. S. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100222 · Cited by 497 · see citing works
Deep Transfer Learning Based Classification Model for COVID-19 Disease
Pathak, Y., Shukla, P. K., Tiwari, A., Stalin, S., Singh, S., & Shukla, P. K. (2020) · IRBM · doi:10.1016/j.irbm.2020.05.003 · Cited by 462 · see citing works
Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT imagesPreprint
Song, Y., Zheng, S., Li, L., et al. (2020) · medRxiv · doi:10.1101/2020.02.23.20026930 · Cited by 332 · see citing works
Toğaçar, M., Ergen, B., & Cömert, Z. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2019.109531 · Cited by 310 · see citing works
Jin, S., Wang, B., Xu, H., et al. (2020) · medRxiv · doi:10.1101/2020.03.19.20039354 · Cited by 306 · see citing works
Time–Frequency Representation and Convolutional Neural Network-Based Emotion Recognition
Khare, S. K., & Bajaj, V. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3008938 · Cited by 299 · see citing works
Kang, H., Xia, L., Yan, F., Wan, Z., Shi, F., Yuan, H., Jiang, H., Wu, D., Sui, H., Zhang, C., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2992546 · Cited by 243 · see citing works
Chang, T., Wu, J.-L., & Chang, L. (2020) · Journal of the Formosan Medical Association · doi:10.1016/j.jfma.2020.04.007 · Cited by 242 · see citing works
Tuncer, T., Doğan, Ş., & Özyurt, F. (2020) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2020.104054 · Cited by 177 · see citing works
An Improved k-Nearest Neighbor Classification Using Genetic Algorithm
Suguna, N., & Thanushkodi, K. (2010) · Cited by 167 · see citing works
Budak, Ü., Cömert, Z., Rashid, Z. N., Şengür, A., & Çıbuk, M. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105765 · Cited by 167 · see citing works
Self-citationA Comprehensive Survey on Support Vector Machine in Data Mining Tasks: Applications & Challenges
Nayak, J., Naik, B., & Behera, H. S. (2015) · International Journal of Database Theory and Application · doi:10.14257/ijdta.2015.8.1.18 · Cited by 146 · see citing works
Toğaçar, M., Ergen, B., & Cömert, Z. (2019) · Measurement · doi:10.1016/j.measurement.2019.107459 · Cited by 128 · see citing works
Self-citationDeep Learning Applications for Hyperspectral Imaging: A Systematic Review
Özdemir, A., & Polat, K. (2020) · Journal of the Institute of Electronics and Computer · doi:10.33969/jiec.2020.21004 · Cited by 125 · see citing works
Self-citationToğaçar, M., Ergen, B., & Cömert, Z. (2019) · Medical Hypotheses · doi:10.1016/j.mehy.2019.109503 · Cited by 95 · see citing works
Self-citationConvolutional neural network approach for automatic tympanic membrane detection and classification
Başaran, E., Cömert, Z., & Çelik, Y. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101734 · Cited by 86 · see citing works
Self-citationSurface EMG signals and deep transfer learning-based physical action classification
Demir, F., Bajaj, V., İnce, M. C., et al. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04553-7 · Cited by 69 · see citing works
Arıcan, M., & Polat, K. (2020) · Journal of Artificial Intelligence and Systems · doi:10.33969/ais.2020.21003 · Cited by 47 · see citing works
Self-citationHybrid Computerized Method for Environmental Sound Classification
Ullo, S. L., Khare, S. K., Bajaj, V., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3006082 · Cited by 47 · see citing works
Robust Approach Based on Convolutional Neural Networks for Identification of Focal EEG Signals
Bajaj, V., Taran, S., Tanyıldızı, E., et al. (2019) · IEEE Sensors Letters · doi:10.1109/lsens.2019.2909119 · Cited by 19 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [20]Muhammad E.H.C. Tawsifur R. Amith K. Rashid M. Muhammad Abdul K. Zaid Bin M. COVID-19 radiology database. Can AI help screen viral COVID-19 pneumonia? 2020 1 14 https://arxiv.org/abs/2003.13145 · arXiv
- [36]Rokach L. Maimon O. Decision Trees Data Min. Knowl. Discov. Handb. 2005 Springer US Boston, MA 165 192 10.1007/0-387-25465-X · DOI
Cited by
Sorted by citations
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906 · see citing works
Federated learning for COVID-19 screening from Chest X-ray images
Feki, I., Ammar, S., Kessentini, Y., & Muhammad, K. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107330 · Cited by 278 · see citing works
A systematic review on AI/ML approaches against COVID-19 outbreak
Dogan, O., Tiwari, S., Jabbar, M. A., & Guggari, S. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00424-8 · Cited by 87 · see citing works
- Authors
- Nour, M., Cömert, Z., & Polat, K.
- Affiliations
- King Abdulaziz University (SA)
- Samsun University (TR)
- Bolu Abant İzzet Baysal University (TR)
- Corresponding author
- Polat, Kemal
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 97
- Pages
- 106580
- ISSN
- 1568-4946 (ISSN-L)
- PMC
- PMC7385069
- OpenAlex
- W3044240928
- Cited by
- 316
- References
- 49
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 25 · 2023: 53 · 2022: 85 · 2021: 110 · 2020: 21
- FWCI
- 32.75
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Applied Soft Computing (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- King Abdulaziz University
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- COVID-19 detection, chest X-ray images, computer-aided diagnosis, convolutional neural networks, deep feature extraction, Bayesian optimization, hyperparameter optimization, support vector machine, k-nearest neighbors, decision tree, COVID-19 pneumonia, radiology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
