{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T10:33:07Z","timestamp":1722249187536},"reference-count":34,"publisher":"National Library of Serbia","issue":"3","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:p>Deep learning plays a major role in detecting the presence of Coronavirus 2019 (COVID-19) and demands huge data. Availability of annotated data is a hurdle in using Deep learning technique. To enhance the accuracy of detection Deep Convolutional Generative Adversarial Network (DCGAN) is used to generate synthetic data. Densenet-201 is identified as the deep learning framework to detect COVID-19 from X-ray images. In this research, to validate the effectiveness of the Densenet-201, we explored conventional machine learning approaches such as SVM, Random Forest and Convolutional Neural Network (CNN). The feature map for training the machine learning approaches are extracted using Densenet-201 as feature extractor. The results show that Densenet-201 as feature representation with SVM is performing well in detecting COVID-19 with high accuracy. Moreover we experimented the proposed methodology without using DCGAN as well. DenseNet-201 based approach is capable of detecting the presence of COVID-19 with high accuracy. Experiments demonstrated that the proposed transfer learning approach based on DenseNet-201 along with DCGAN based augmentation outperforms the State of the art approaches like ResNet50, CNN, and VGG-16.<\/jats:p>","DOI":"10.2298\/csis220207033b","type":"journal-article","created":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T14:37:16Z","timestamp":1663598236000},"page":"1241-1259","source":"Crossref","is-referenced-by-count":3,"title":["Efficient generative transfer learning framework for the detection of COVID-19"],"prefix":"10.2298","volume":"19","author":[{"given":"J.","family":"Bhuvana","sequence":"first","affiliation":[{"name":"Sri Sivasubramaniya Nadar College of Engineering"}]},{"given":"T.T.","family":"Mirnalinee","sequence":"additional","affiliation":[{"name":"Sri Sivasubramaniya Nadar College of Engineering"}]},{"given":"B.","family":"Bharathi","sequence":"additional","affiliation":[{"name":"Sri Sivasubramaniya Nadar College of Engineering"}]},{"given":"Infant","family":"Sneha","sequence":"additional","affiliation":[{"name":"Sri Sivasubramaniya Nadar College of Engineering"}]}],"member":"1078","reference":[{"key":"ref1","unstructured":"Overview of GAN Structure). https:\/\/developers.google.com\/machine-learning\/gan\/gan_structure (April 22, 2019)"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Abbas, A., Abdelsamea, M.M., Gaber, M.M.: Classification of covid-19 in chest x-ray images using detrac deep convolutional neural network. Applied Intelligence 51(2), 854-864 (2021)","DOI":"10.1007\/s10489-020-01829-7"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Ahmed, S., Yap, M.H., Tan, M., Hasan, M.K.: Reconet: Multi-level preprocessing of chest x-rays for covid-19 detection using convolutional neural networks. medRxiv (2020)","DOI":"10.1101\/2020.07.11.20149112"},{"key":"ref4","unstructured":"Anand, A., Anandan, K.R., Jayaraman, B., Thai, M.T.N.T.: Simple Neural Network based TB Classification. Proceedings of the Working Notes of CLEF 2021 2936, 1145-1150 (2021)"},{"key":"ref5","unstructured":"Balwal, U., Yeragudipati, S.A., Bhuvana, J., Mirnalinee, T.T.: Deep learning based tb severity prediction. In: CLEF (Working Notes) (2020)"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Basavegowda, H.S., Dagnew, G.: Deep learning approach for microarray cancer data classification. CAAI Trans. Intell. Technol. 5(1), 22-33 (2020)","DOI":"10.1049\/trit.2019.0028"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Bhuvana. J, Mirnalinee, T.T.: An approach to plant disease detection using deep learning techniques. ITECKNE 18(2), 1-14 (2021)","DOI":"10.15332\/iteckne.v18i2.2615"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Da\u02dbbrowski,M.,Michalik, T.: How effective is transfer learning method for image classification. In: Proceedings of the Position Papers of the 2017 Federated Conference on Computer Science and Information Systems. vol. 12, pp. 3-9 (2017)","DOI":"10.15439\/2017F526"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Gao, T.: Chest x-ray image analysis and classification for covid-19 pneumonia detection using deep cnn. medRxiv (2020)","DOI":"10.21203\/rs.3.rs-64537\/v2"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. science 313(5786), 504-507 (2006)","DOI":"10.1126\/science.1127647"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al.: Clinical features of patients infected with 2019 novel coronavirus in wuhan, china. The lancet 395(10223), 497-506 (2020)","DOI":"10.1016\/S0140-6736(20)30183-5"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4700-4708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Jaisakthi, M.S., Mirunalini, P., Thenmozhi, D., Muthukumar, V.: Fish species recognition using transfer learning techniques. International Journal of Advances in Intelligent Informatics 7(2), 188-197 (2021)","DOI":"10.26555\/ijain.v7i2.610"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., Kaur, M.: Classification of the covid-19 infected patients using densenet201 based deep transfer learning. Journal of Biomolecular Structure and Dynamics pp. 1-8 (2020)","DOI":"10.1080\/07391102.2020.1788642"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Kavitha, S., Poornima, S., Sitara, N.S., Sarada Devi, A.: Classification of lung tuberculosis using non parametric and deep neural network techniques. In: 2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP). pp. 1-5 (2020)","DOI":"10.1109\/ICCCSP49186.2020.9315211"},{"key":"ref16","unstructured":"Korkmaz, A.: Prediction from x-ray images (resnet50). https:\/\/www.kaggle.com\/ahmetkorkmaz\/prediction-from-xray-images-resnet50 (2021, January 25)"},{"key":"ref17","unstructured":"Korkmaz, A.: Prediction from x-ray images(vgg16). https:\/\/www.kaggle.com\/ahmetkorkmaz\/prediction-from-xray-images-vgg16 (2021, January 25)"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Lu, M.T., Ivanov, A., Mayrhofer, T., Hosny, A., Aerts, H.J., Hoffmann, U.: Deep learning to assess long-term mortality from chest radiographs. JAMA network open 2(7), e197416-e197416 (2019)","DOI":"10.1001\/jamanetworkopen.2019.7416"},{"key":"ref19","unstructured":"Luis, M.: Convolutional neural networks to detect lung disease in Chest X-ray images. https:\/\/www.kaggle.com\/marcelor\/cnn-chestxray-87-f1-score\/notebook (2020, November 17)"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Maghdid, H.S., Asaad, A.T., Ghafoor, K.Z., Sadiq, A.S., Mirjalili, S., Khan, M.K.: Diagnosing covid-19 pneumonia from x-ray and ct images using deep learning and transfer learning algorithms. In: Multimodal Image Exploitation and Learning 2021. vol. 11734, p. 117340E. International Society for Optics and Photonics (2021)","DOI":"10.1117\/12.2588672"},{"key":"ref21","unstructured":"Marimuthu S, Bhuvana, J., Mirnalinee, T.T.: Disease detection in tomato plants using deep learning. Intelligent Systems and Computer Technology, Advances in Parallel Computing 37, 190-195 (2020)"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Nishio, M., Noguchi, S., Matsuo, H., Murakami, T.: Automatic classification between covid- 19 pneumonia, non-covid-19 pneumonia, and the healthy on chest x-ray image: combination of data augmentation methods. Scientific reports 10(1), 1-6 (2020)","DOI":"10.1038\/s41598-020-74539-2"},{"key":"ref23","unstructured":"Patel, P.: Chest X-ray (Covid-19 & Pneumonia). https:\/\/www.kaggle.com\/prashant268\/chest-xray-covid19-pneumonia (2020, September 17)"},{"key":"ref24","unstructured":"Pathak, Y., Shukla, P.K., Tiwari, A., Stalin, S., Singh, S.: Deep transfer learning based classification model for covid-19 disease. Irbm (2020)"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Punn, N.S., Agarwal, S.: Automated diagnosis of covid-19 with limited posteroanterior chest x-ray images using fine-tuned deep neural networks. Applied Intelligence 51(5), 2689-2702 (2021)","DOI":"10.1007\/s10489-020-01900-3"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Sedik, A., Iliyasu, A.M., El-Rahiem, A., Abdel Samea, M.E., Abdel-Raheem, A., Hammad, M., Peng, J., El-Samie, A., Fathi, E., El-Latif, A., et al.: Deploying machine and deep learning models for efficient data-augmented detection of covid-19 infections. Viruses 12(7), 769 (2020)","DOI":"10.3390\/v12070769"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Singhal, T.: A review of coronavirus disease-2019 (covid-19). The indian journal of pediatrics 87(4), 281-286 (2020)","DOI":"10.1007\/s12098-020-03263-6"},{"key":"ref28","unstructured":"Sladojevic, M.A.S.S.S.: Detection of covid-19 cases by utilizing deep learning algorithms on x-ray images. In: Proceedings of the 18th International Scientific Conference on Industrial Systems Industrial Innovation in Digital Age. pp. 1-8"},{"key":"ref29","unstructured":"Tang, Y.: Deep learning using support vector machines. CoRR, abs\/1306.0239 2 (2013)"},{"key":"ref30","unstructured":"Vrba\u010di\u010d, G., Pe\u010dnik, \u0160., Podgorelec, V.: Hyper-parameter optimization of convolutional neural networks for classifying covid-19 x-ray images. Computer Science and Information Systems (00), 56-56 (2021)"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Wang, L., Lin, Z.Q.,Wong, A.: Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports 10(1), 1-12 (2020)","DOI":"10.1038\/s41598-020-76550-z"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"Wang, S.H., Zhang, Y.D.: Densenet-201-based deep neural network with composite learning factor and precomputation for multiple sclerosis classification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16(2s), 1-19 (2020)","DOI":"10.1145\/3341095"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"Wang,W., Xu, Y., Gao, R., Lu, R., Han, K.,Wu, G., Tan,W.: Detection of sars-cov-2 in different types of clinical specimens. Jama 323(18), 1843-1844 (2020)","DOI":"10.1001\/jama.2020.3786"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J., Li, W., Sun, Z., He, J., Li, Y., Shen, C., et al.: Viral pneumonia screening on chest x-ray images using confidence-aware anomaly detection. arXiv preprint arXiv:2003.12338 (2020)","DOI":"10.1109\/TMI.2020.3040950"}],"container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:19:45Z","timestamp":1691741985000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02142200033B"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":34,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022]]}},"URL":"https:\/\/doi.org\/10.2298\/csis220207033b","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}