MODIFY CONVOLUTIONAL NEURAL NETWORK MODEL FOR THE DIAGNOSIS OF MULTI-CLASSES LUNG DISEASES COVID-19 AND PNEUMONIA BASED ON X-RAY IMAGES

  • OMER SEDQI KAREEM Dept. of Electrical and Computer Engineering, College of Engineering, University of Duhok, Kurdistan Region-Iraq
  • AHMED KHORSHEED AL-SULAIFANIE Dept. of Electrical and Computer Engineering, College of Engineering, University of Duhok, Kurdistan Region-Iraq
Keywords: Convolutional neural network, COVID-19, Deep learning, X-Ray images, Pneumonia, Artificial neural network

Abstract

COVID-19 is a new virus able to infect both the upper and lower respiratory lobes of ling. There is a daily increase in cases and deaths in the global epidemic. A number of the test kits now in use are sluggish and in short supply; hence RT-PCR testing is the most appropriate option. To avoid a potentially fatal outcome, early detection of COVID-19 is essential. According to numerous studies, visual markers (abnormalities) on a patient’s Chest X-Ray imaging can be a valuable characteristic of a COVID-19 patient, which can be exploited to discover the virus. In this research, Convolutional Neural Networks (CNNs) are being proposed to detect the Covid-19 disease based on X-rays images. The suggested model is based on modified VGG16 architecture for deep feature extraction. The fine-tuning approach with end-to-end training is also tilized in the aforementioned deep CNN models. The suggested model has been trained and evaluated on the dataset contains 7,245 X-ray images, comprising 1,420 Covid-19 cases, 4,167 bacterial cases of Pneumonia, and 1,658 normal cases. The model is evaluated using metrics such as accuracy, precision, recall, and f1-score. The proposed model enhanced the accuracy by using less trainable parameters (weights) than the Vgg16 model. Thus, the time needed for training and testing will be less. In addition, it achieved a multiclass micro-average of 97% precision, 97% recall, 97% f1-score, and 97% classification accuracy. The findings obtained show that the proposed strategy outperforms several currently used methods. This model appears to be convenient and forceful for multiclass classification

Downloads

Download data is not yet available.

References

S. Thakur and A. Kumar, "X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN)," Biomed. Signal Process. Control, vol. 69, p. 102920, 2021, doi: https://doi.org/10.1016/j.bspc.2021.102920.
X. Xu et al., “A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia,” Engineering, 2020, doi: 10.1016/j.eng.2020.04.010.
K. Pang, L. Jin, and Z. Zhang, "Systematic application of COVID-19 nucleic acid tests in general surgery departments in China: An update of current status with nationwide survey data," Int. J. Surg., vol. 82, pp. 100–102, 2020, doi: https://doi.org/10.1016/j.ijsu.2020.08.011.
R. Jain, M. Gupta, S. Taneja, and D. J. Hemanth, "Deep learning based detection and analysis of COVID-19 on chest X-ray images," Appl. Intell., vol. 51, no. 3, pp. 1690–1700, 2021, doi: 10.1007/s10489-020-01902-1.
J. Sun, X. Li, C. Tang, S.-H. Wang, and Y.-D. Zhang, "MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images," Knowledge-Based Syst., vol. 232, p. 107494, 2021, doi: https://doi.org/10.1016/j.knosys.2021.107494.
A. Kumar, A. R. Tripathi, S. C. Satapathy, and Y.-D. Zhang, "SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network," Pattern Recognit., vol. 122, p. 108255, 2022, doi: https://doi.org/10.1016/j.patcog.2021.108255.
A. Narin, C. Kaya, and Z. Pamuk, "Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks," arXiv Prepr. arXiv2003.10849., 2020.
M. Loey, F. Smarandache, and N. E. M. Khalifa, "Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning," Symmetry (Basel)., vol. 12, no. 4, 2020, doi: 10.3390/SYM12040651.
I. D. Apostolopoulos and T. A. Mpesiana, "Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks," Phys. Eng. Sci. Med., vol. 43, no. 2, pp. 635–640, 2020, doi: 10.1007/s13246-020-00865-4.
K. El Asnaoui and Y. Chawki, "Using X-ray images and deep learning for automated detection of coronavirus disease," J. Biomol. Struct. Dyn., 2020, doi: 10.1080/07391102.2020.1767212.
A. I. Khan, J. L. Shah, and M. Bhat, "CoroNet: A Deep Neural Network for Detection and Diagnosis of Covid-19 from Chest X-ray Images," arXiv, vol. 196, 2020.
S. Toraman, T. B. Alakus, and I. Turkoglu, "Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks," Chaos, Solitons and Fractals, 2020, doi: 10.1016/j.chaos.2020.110122.
C. Ouchicha, O. Ammor, and M. Meknassi, "CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images," Chaos, Solitons and Fractals, vol. 140, 2020, doi: 10.1016/j.chaos.2020.110245.
A. Abbas, M. M. Abdelsamea, and M. M. Gaber, "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.
M. Heidari, S. Mirniaharikandehei, A. Z. Khuzani, G. Danala, Y. Qiu, and B. Zheng, "Improving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms," arXiv, vol. 144, no. June, 2020.
M. Chakraborty, S. V. Dhavale, and J. Ingole, "Corona-Nidaan: lightweight deep convolutional neural network for chest X-Ray based COVID-19 infection detection," Appl. Intell., vol. 51, no. 5, pp. 3026–3043, 2021, doi: 10.1007/s10489-020-01978-9.
S. A. B. P and C. S. R. Annavarapu, "Deep learning-based improved snapshot ensemble technique for COVID-19 chest X-ray classification," Appl. Intell., vol. 51, no. 5, pp. 3104–3120, 2021, doi: 10.1007/s10489-021-02199-4.
"Curated Chest X-Ray Image Dataset for COVID-19 | Kaggle." https://www.kaggle.com/unaissait/curated-chest-xray-image-dataset-for-covid19 (accessed Aug. 30, 2021).
"No Title," kaggle.com. https://www.kaggle.com/prashant268/chest-xray-covid19-pneumonia.
D. A. Dias Júnior et al., "Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost," Expert Syst. Appl., vol. 183, p. 115452, 2021, doi: https://doi.org/10.1016/j.eswa.2021.115452.
S. Shah, H. Mehta, and P. Sonawane, "Pneumonia detection using convolutional neural networks," Proc. 3rd Int. Conf. Smart Syst. Inven. Technol. ICSSIT 2020, no. Icssit, pp. 933–939, 2020, doi: 10.1109/ICSSIT48917.2020.9214289.
O. Stephen, M. Sain, U. J. Maduh, and D. U. Jeong, "An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare," J. Healthc. Eng., 2019, doi: 10.1155/2019/4180949.
I. Apostolopoulos and M. Tzani, "Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks," Australas. Phys. Eng. Sci. Med., vol. 43, 2020, doi: 10.1007/s13246-020-00865-4.
T. Rahman et al., "Transfer learning with deep Convolutional Neural Network (CNN) for pneumonia detection using chest X-ray," Appl. Sci., 2020, doi: 10.3390/app10093233.
Published
2022-04-12
How to Cite
KAREEM, O. S., & AL-SULAIFANIE, A. K. (2022). MODIFY CONVOLUTIONAL NEURAL NETWORK MODEL FOR THE DIAGNOSIS OF MULTI-CLASSES LUNG DISEASES COVID-19 AND PNEUMONIA BASED ON X-RAY IMAGES. Journal of Duhok University, 25(1), 63-73. https://doi.org/10.26682/sjuod.2022.25.1.9
Section
Pure and Engineering Sciences