171
Views
2
CrossRef citations to date
0
Altmetric
Articles

DNN based approach to classify Covid’19 using convolutional neural network and transfer learning

, ORCID Icon, &
Pages 907-919 | Received 14 May 2021, Accepted 15 Sep 2021, Published online: 01 Oct 2021

References

  • WHO. Emergencies preparedness, response [Online]. Available from: https://www.who.int/csr/don/05-january-2020-pneumonia-of-unkown-cause-china/en/?mod=article.
  • COVID-19 Map – Johns Hopkins Coronavirus Resource Center. Available from: https://coronavirus.jhu.edu/map.html (accessed Jul 10, 2021).
  • Medicine TLR. COVID-19 transmission—up in the air. Lancet Respir Med. 2020; doi:10.1016/S2213-2600(20)30514-2.
  • Guglielmi G. Fast Coronavirus tests: what they can and can’t do. Nature. 2020;585(7826):496–498. doi:10.1038/d41586-020-02661-2.
  • Makris A, Kontopoulos I, Tserpes K. COVID-19 detection from chest X-Ray images using deep learning and convolutional neural networks. In: Spyropoulos, C, Varlamis, I, Androutsopoulos, I, Malakasiotis, P editors. 11th Hellenic Conference on Artificial Intelligence; SETN 2020. New York (NY): Association for Computing Machinery; 2020. p. 60–66. doi:10.1145/3411408.3411416
  • Chandra TB, Verma K, Singh BK, et al. Coronavirus disease (COVID-19) detection in chest X-ray images using majority voting based classifier ensemble. Expert Syst Appl. 2021;165:113909. doi:10.1016/j.eswa.2020.113909.
  • Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-Ray images using DeTraC deep convolutional neural network. Appl Intell. 2020; doi:10.1007/s10489-020-01829-7.
  • Chronic respiratory diseases. Available from: https://www.who.int/westernpacific/health-topics/chronic-respiratory-diseases (accessed Feb 27, 2021).
  • Tuberculosis (TB). Available from: https://www.who.int/news-room/fact-sheets/detail/tuberculosis (accessed Feb 27, 2021).
  • Gupta S, Chandra S, Maheswari N, et al. A model for screening eye diseases using optical coherence Tomography images. Int J Comput Appl. 2020; 1–5. doi:10.1080/1206212X.2020.1759857.
  • Ismael AM, Şengür A. Deep learning approaches for COVID-19 detection based on chest X-Ray images. Expert Syst Appl. 2021;164:114054. doi:10.1016/j.eswa.2020.114054.
  • Kesim E, Dokur Z, Olmez T. X-Ray chest image classification by a small-sized convolutional neural network. In 2019 Scientific Meeting on Electrical-Electronics Biomedical Engineering and Computer Science (EBBT); 2019;p. 1–5. https://doi.org/10.1109/EBBT.2019.8742050.
  • Rajpurkar P, Irvin J, Ball RL, et al. Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018;15(11):e1002686. doi:10.1371/journal.pmed.1002686.
  • Panwar H, Gupta PK, Siddiqui MK, et al. A deep learning and Grad-CAM based color visualization approach for fast detection of COVID-19 cases using Chest X-Ray and CT-Scan images. Chaos, Solitons Fractals. 2020;140:110190, doi:10.1016/j.chaos.2020.110190.
  • Pk S, Sk B. Detection of coronavirus disease (COVID-19) based on deep features. 2020. doi:10.20944/preprints202003.0300.v1.
  • Wang L, Wong A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. arXiv:2003.09871 [cs, eess], 2020.
  • Gupta, A.; Anjum, N.; Gupta, S.; Katarya, R. InstaCovNet-19: a deep learning classification model for the detection of COVID-19 patients using chest X-ray. Appl Soft Comput, 2021, 99, 106859. doi:10.1016/j.asoc.2020.106859.
  • Chowdhury MEH, Rahman T, Khandakar A, et al. Can AI help in screening viral and COVID-19 pneumonia? IEEE Access. 2020;8:132665–132676. doi:10.1109/ACCESS.2020.3010287.
  • Ouchicha C, Ammor O, Meknassi M. CVDNet: a novel deep learning architecture for detection of coronavirus (covid-19) from chest X-ray images. Chaos, Solitons Fractals. 2020;140:110245. doi:10.1016/j.chaos.2020.110245.
  • Karim MR, Döhmen T, Rebholz-Schuhmann D, et al. DeepCOVIDExplainer: explainable COVID-19 diagnosis based on chest X-ray images. arXiv:2004.04582 [cs, eess], 2020.
  • Phankokkruad M. COVID-19 pneumonia detection in chest X-ray images using transfer learning of convolutional neural networks. In Proceedings of the 3rd International Conference on Data Science and Information Technology; DSIT 2020; Association for Computing Machinery: New York, NY, USA, 2020; p. 147–152. doi:10.1145/3414274.3414496.
  • Akram T, Attique M, Gul S, et al. A novel framework for rapid diagnosis of COVID-19 on computed tomography scans. Pattern Anal Appl. 2021: 1–14. doi:10.1007/s10044-020-00950-0.
  • Mahmud T, Rahman MA, Fattah SA. CovXNet: a multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization. Comput Biol Med. 2020;122:103869. doi:10.1016/j.compbiomed.2020.103869.
  • Uçar E, Atila Ü, Uçar M, et al. Automated detection of covid-19 disease using deep fused features from chest radiography images. Biomed Signal Process Control. 2021;69:102862. doi:10.1016/j.bspc.2021.102862.
  • Chauhan T, Palivela H, Tiwari S. Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging. Int J Inf Manag Data Insights. 2021;1(2):100020. doi:10.1016/j.jjimei.2021.100020.
  • Ahmad F, Farooq A, Ghani MU. Deep ensemble model for classification of novel coronavirus in chest X-ray images. Comput Intell Neurosci. 2021;2021:e8890226. doi:10.1155/2021/8890226.
  • Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015;115(3):211–252. doi:10.1007/s11263-015-0816-y.
  • Zhang F, Li Z, Zhang B, et al. Multi-modal deep learning model for auxiliary diagnosis of Alzheimer’s disease. Neurocomputing. 2019;361:185–195. doi:10.1016/j.neucom.2019.04.093.
  • Sandler M, Howard A, Zhu M, et al. MobileNetV2: inverted residuals and linear bottlenecks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2018; p. 4510–4520. doi:10.1109/CVPR.2018.00474.
  • Nair V, Hinton GE. Rectified linear units improve restricted Boltzmann Machines; 2010.
  • Zeiler MD, Ranzato M, Monga R, et al. On rectified linear units for speech processing. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing; 2013; p. 3517–3521. doi:10.1109/ICASSP.2013.6638312.
  • Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics; JMLR Workshop and Conference Proceedings, 2011; p. 315–323.
  • Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 2014;15(1):1929–1958.
  • Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks. arXiv:1608.06993 [cs], 2018.
  • Ron Kikinis, C.-F. W., Knutsson, H. 2 – Adaptive image filtering. In Bankman, IN, editor. Handbook of medical imaging biomedical engineering. San Diego: Academic Press; 2000. p. 19–31. doi:10.1016/B978-012077790-7/50005-9.
  • Khoong WH. COVID-19 Xray dataset (Train & Test Sets). Available from: https://kaggle.com/khoongweihao/covid19-xray-dataset-train-test-sets(accessed Nov 23, 2020).
  • Govi P. Corona hack -chest X-ray-dataset. Available from: https://kaggle.com/praveengovi/coronahack-chest-xraydataset (accessed Nov 23, 2020).
  • Cohen JP, Morrison P, Dao L. COVID-19 image data collection. arXiv:2003.11597 [cs, eess, q-bio], 2020.
  • Mooney P. Chest X-ray images (Pneumonia). Available from: https://kaggle.com/paultimothymooney/chest-xray-pneumonia (accessed Nov 23, 2020).
  • Cohen JP. ieee8023/covid-chest X ray-dataset. Available from: https://github.com/ieee8023/covid-chestxray-dataset (accessed Nov 23, 2020).
  • Tharwat A. Classification assessment methods. Appl Comput Inform. 2020; ahead-of-print (ahead-of-print). doi:10.1016/j.aci.2018.08.003.
  • Sokolova M, Japkowicz N, Szpakowicz S, et al. Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Sattar A, Kang B, Hutchison D, Kanade T, Kittler J, Kleinberg JM, Mattern F, Mitchell JC, Naor M, Nierstrasz O, Pandu Rangan C, Steffen B, editors. AI 2006: advances in artificial intelligence. Berlin: Springer Berlin Heidelberg; 2006. p. 1015–1021. (Series eds. Lecture notes in Computer science; vol 4304). doi:10.1007/11941439_114.
  • Kallenberg M, Petersen K, Nielsen M, et al. Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging. 2016;35(5):1322–1331. doi:10.1109/TMI.2016.2532122.
  • Suk H-I, Lee S-W, Shen D. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage. 2014;101:569–582. doi:10.1016/j.neuroimage.2014.06.077.
  • Chan S, Dittakan K, Salhi SE. Osteoarthritis detection by applying quadtree analysis to human joint knee X-Ray imagery. Int J Comput Appl. 2020;1–8. doi:10.1080/1206212X.2020.1838145.
  • Quin C, Yao D, Shi Y. Computer-aided detection in chest radiography based on artificial intelligence: a survey | SpringerLink. Springer BioMed Eng OnLine. 2018;17; doi:10.1186/s12938-018-0544-y.
  • Sharma AK, Aggarwal G, Bhardwaj S, et al. Classification of Indian classical music with time-series matching deep learning approach. IEEE Access. 2021;9:102041–102052.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.