COVID-19 Diagnosis System using SimpNet Deep Model

Main Article Content

Tarza Hasan Abdullah
https://orcid.org/0000-0001-9225-8188
Fattah Alizadeh
https://orcid.org/0000-0002-0690-1147
Berivan Hasan Abdullah
https://orcid.org/0000-0002-9038-2297

Abstract

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings, and Pneumonia) classification tasks. Our model has achieved an accuracy value of 98.4% for binary and 93.8% for the multi-class classification. The number of parameters of our model is 11 Million parameters which are fewer than some state-of-the-art methods with achieving higher results.

Downloads

Download data is not yet available.

Article Details

How to Cite
1.
Abdullah TH, Alizadeh F, Abdullah BH. COVID-19 Diagnosis System using SimpNet Deep Model. Baghdad Sci.J [Internet]. 2022 Oct. 1 [cited 2022 Nov. 30];19(5):1078. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6074
Section
article

References

Future of Mathematical Modelling: A Review of COVID-19 Infected Cases Using S-I-R Model. Baghdad Sci. J [Internet]. 2021 Mar 30 [cited 2021 Jul 31];18(1(Suppl.)). https://doi.org/10.21123/bsj.2021.18.1(Suppl.).0824

Wu F, Zhao S, Yu B, Chen Y-M, Wang W, Song Z-G, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020 Mar; 579 (7798): 265–9.

Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020 Feb; 395 (10223): 497–506.

Pereira RM, Bertolini D, Teixeira LO, Silla CN, Costa YMG. COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput. Methods Programs Biomed. 2020 Oct; 194: 105532.

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:2003.09871v4. 2020 May, 11 :1-12

Rabani ST, Khan QR, Khanday AMUD. Detection of Suicidal Ideation on Twitter using Machine Learning & Ensemble Approaches. Baghdad Sci. J. 2020;17(4):1328.

Hasanpour SH, Rouhani M, Fayyaz M, Sabokrou M, Adeli E. Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet. arXiv:1802.06205v1,2018 Feb:1-13

Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, et al. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology. 2020 Apr;295(1):202–7.

Soffer S, Ben-Cohen A, Shimon O, Amitai MM, Greenspan H, Klang E. Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide. Radiology. 2019 Mar;290(3):590–606.

Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020 Jun;43(2):635–40.

Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition.

arXiv:2015. 14091556 Cs. 2020 Nov, 26

Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:2017. 170404861. 2020 Dec, 24

Szegedy C, Ioffe S, Vanhoucke V, Alemi A. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. arXiv:2016. 160207261. 2020 Dec, 24

Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions. Proc. IEEE Comput Soc Conf Comput Vis. Pattern Recognit: 1800–7. Available from: http://ieeexplore.ieee.org/document/8099678.

Alom MZ, Hasan M, Yakopcic C, Taha TM, Asari VK. Improved inception-residual convolutional neural network for object recognition. Neural Comput. Appl. 2020 Jan;32(1):279–93.

He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. arXiv:2015. 151203385 Cs. 2020 Nov, 26

Alom Z, Rahman MMS, Nasrin MS, Taha TM, Asari VK. COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches. Neural Comput Applic. 2020 Jan;32(1):279–93.

Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med. 2020 Jun;121:103792.

Redmon J, Farhadi A. YOLO9000: Better, Faster, Stronger. arXiv:2015. 161208242 Cs. 2020 Dec, 24

Khan AI, Shah JL, Bhat MM. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput. Methods Programs Biomed. 2020 Nov; 196:105581.

Toraman S, Alakus TB, Turkoglu I. Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos Solitons Fractals. 2020 Nov; 140:110122.

Khobahi S, Agarwal C, Soltanalian M. CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray Images [Internet]. Infectious Diseases (except HIV/AIDS); 2020 Apr [cited 2020 Dec 26]. Available from: http://medrxiv.org/lookup/doi/10.1101/2020.04.14.20065722

Cohen JP, Morrison P, Dao L. COVID-19 Image Data Collection. arXiv:2020. 200311597 Cs. 2020 Dec, 24

Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. 2017 IEEE Conf. Comput. Vis. Pattern Recognit. CVPR. 2017 Jul;3462–71.