Detection of COVID-19 from radiology modalities and identification of prognosis patterns

Document Type : Research Paper


1 Department of IT, V R Siddhartha Engineering College, India

2 College of Medicine, University of Anbar, Ramadi, Iraq

3 Department of Nursing Sciences, Shirvan Branch, Islamic Azad University, Shirvan, Iran

4 Department of Computer Science, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq

5 Department of Electricity Engineering College of Engineering, University of Tikrit, Tikrit, Iraq

6 Department of Communication Engineering Collage of Engineering, University of Diyala: Baqubah, Diyala, Iraq

7 Directorate of Private University Education, Ministry of Higher Education, Baghdad, Iraq

8 Department of Computer Engineering Collage of Engineering, University of Diyala: Baqubah, Diyala, Iraq


SARS-CoV-2 and the consequential COVID-19 virus is one of the major concerns of the 21st century. Pertaining to the novelty of the disease, it became necessary to discover the efficacy of deep learning techniques in the quick and consistent discovery of COVID-19 based on chest X-ray and CT scan image analysis. In this related work, Prognostic tool using regression was designed for patients with COVID-19 and recognizing prediction patterns to make available important prognostic information on mortality or severity in COVID-19 patients. And reliable convolutional neural network (CNN) architecture models (DenseNet, VGG16, ResNet, Inception Net)to institute whether it would work preeminent in terms of accuracy as well as efficiency with image datasets with Transfer Learning. CNN with Transfer Learning were functional to accomplish the involuntary recognition of COVID-19 from numerary chest X-ray and CT scan images. The experimental results emphasize that selected models, which is formerly broadly tuned through suitable parameters, executes in extensive levels of COVID-19 discovery against pneumonia or normal or lung opacity through the precision of up to 87% for X-Ray and 91% intended for CT scans.


[1] A.S. Abdulbaqi, A.J. Obaid and S.A. Hmeed Alazawi, A smart system for health caregiver based on IoMT: toward telehealth caregiving, Int. J. Online Biomed. Engin. 17(7) (2021) 70–87.
[2] S. Ahuja, B.K. Panigrahi, N. Dey, V. Rajinikanth, T.K. Gandhi, Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices, Appl. Intell. 51 (2021) 571–585.
[3] J. Beutel, H.L. Kundel, R.L. Van Metter and J.M. Fitzpatrick, Handbook of Medical Imaging: Medical Image Processing and Analysis, Bellingham: Spie Press, 2, 2000.
[4] R. Catelli, F. Gargiulo, V. Casola, G. De Pietro, H. Fujita and M. Esposito, A novel COVID-19 data set and an effective deep learning approach for the de-identification of Italian medical records, IEEE Access 9 (2021) 19097–19110.
[5] M.I. Dina, M.E. Nada and M.S. Amany, Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases, Computers in Biology and Medicine 132 (2021) 104348.
[6] P. Dutta, T. Roy and N. Anjum, COVID-19 detection using transfer learning with convolutional neural network, 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), DHAKA, Bangladesh, (2021) 429–432.
[7] R.C. Joshi, S. Yadav, V.K. Pathak, H.S. Malhotra, H.V.S. Khokhar, A. Parihar, N. Kohli, D. Himanshu, R.K. Garg, M.L.B. Bhatt, R. Kumar, N.P. Singh, V. Sardana, R. Burget, C. Alippi, C.M. Travieso-Gonzalez and M.K. Dutta, A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images, Biocyber. Biomed. Engin. 41(1) (2021), 239-254.
[8] A.K. Mishra, S.K. Das, P. Roy and S. Bandyopadhyay, Identifying COVID-19 from chest CT images: A deep convolutional neural networks based approach, J. Health. Engin. 2020 (2020).
[9] T. Ozturk, M. Talo, E.A. Yildirim, U.B. Baloglu, O. Yildirim and U.R. Acharya, Automated detection of COVID-19 cases using deep neural networks with X-ray images, Computers in biology and medicine, 121 (2020) 103792.
[10] H. Panwar, P.K. Gupta, M.K. Siddiqui, R. Morales-Menendez and V. Singh, Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet, Chaos Solitons Fract. 138 (2020) 109944.
[11] S. Pathan, P.C. Siddalingaswamy, T. Ali, Automated detection of COVID-19 from chest X-ray scans using an optimized CNN architecture, Appl. Soft Comput. 104 (2021).
[12] V.S. Rohila, N. Gupta, A. Kaul, D.K. Sharma, Deep learning assisted COVID-19 detection using full CT-scans, Internet of Things 14 (2021).
[13] S. Sheykhivand, Z. Mousavi, S. Mojtahedi, T. Yousefi Rezaii, A. Farzamnia, S. Meshgini and I. Saad, Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images, Alexandria Engin. J. 60(3) (2021) 2885–2903.
[14] D. Singh, V. Kumar, K.M. Vaishali, Classification of COVID-19 patients from chest CT images using multiobjective differential evolution-based convolutional neural networks, Eur. J. Clin. Microbiol. Infect. Dis. 39(7) (2020) 1379–1389.
[15] K.K. Singh and A. Singh, Diagnosis of COVID-19 from chest X-ray images using wavelets-based depthwise convolution network, Big Data Mining Anal., 4(2) (2021) 84–93.
[16] S. Varela-Santos and P. Melin, A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks, Inf. Sci. 545 (2021) 403–414.
[17] Y.-H. Wu, S.-H. Gao, J. Mei, J. Xu, D.-P. Fan, R.-G. Zhang and M.-M. Cheng, JCS: an explainable COVID-19 diagnosis system by joint classification and segmentation, IEEE Trans. Image Proces. 30 (2021) 3113–3126.
[18] S. Ying, S. Zheng, L. Li, X. Zhang, X. Zhang, Z. Huang, J. Chen, H. Zhao, R. Wang, Y. Chong, J. Shen, Y. Zha and Y. Yang, Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images, IEEE/ACM Trans. Comput. Biol. Bioinf. (2020).
Volume 13, Issue 1
March 2022
Pages 1351-1365
  • Receive Date: 29 July 2021
  • Accept Date: 13 September 2021