COVID-19IraqKirkukDataset: Development and evaluation of an Iraqi dataset for COVID-19 classification based on deep learning

Document Type : Research Paper


1 Computer Science Department, College of Computer Science and Information Technology, Kirkuk University, Kirkuk, Iraq

2 Software Department, College of Computer Science and Information Technology, Kirkuk University, Kirkuk, Iraq


In the last two years, the coronavirus (COVID-19) pandemic put healthcare systems around the world under tremendous pressure. There have been intelligent systems (Machine Learning (ML) and Deep Learning (DL)) able to identify COVID-19 from similar normal diseases. The algorithms use Imaging techniques (like Chest X-Rays) in classifying COVID-19. Therefore, many global COVID-19 datasets have been released. However, so far, no public local Iraqi dataset has been developed. Therefore, our contribution is two folds. First, we investigate the techniques of deep learning techniques in COVID-19 classification. Second, we develop a new COVID-19 dataset, namely, “Covid-19IraqKirkukDataset” collected from hospitals in Kirkuk, Iraq. To the best of our knowledge, our dataset is the first COVID-19 dataset. Then, the evaluation of Covid19IraqKirkukDataset using Convolutional Neural Networks (CNNs) demonstrates promising classification outcomes.


[1] A.M. Alqudah, S. Qazan, and A. Alqudah, Automated Systems for Detection of COVID-19 Using Chest X-ray Images and Lightweight Convolutional Neural Networks, preprint, In Review, April 2020.
[2] I.D. Apostolopoulos and T.A. Mpesiana, Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks, Phys. Engin. Sci. Medic. 43 (2020), no. 2, 635–640.
[3] P.R.A.S. Bassi and R. Attux, A deep convolutional neural network for COVID-19 detection using chest X-rays, Res. Biomed. Engin. 38 (2022), no. 1, 139–148.
[4] L. Cai, J. Gao, and D. Zhao, A review of the application of deep learning in medical image classification and segmentation, Ann. Translat. Medic. 8 (2020), no. 11, 713–713.
[5] L. Falzone, G. Gattuso, A. Tsatsakis, D. Spandidos, and M. Libra, Current and innovative methods for the diagnosis of COVID-19 infection (Review), Int. J. Molecul. Medic. 47 (2021), no. 6, 100.
[6] E. Hussain, M. Hasan, M.A. Rahman, I. Lee, T. Tamanna, and M.Z. Parvez, CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images, Chaos Solitons Fractals 142 (2021), 110495.
[7] E. Irmak, A novel deep convolutional neural network model for COVID-19 disease detection, 2020 Medical Technologies Congress (TIPTEKNO) (Antalya, Turkey), IEEE, November 2020, pp. 1–4.
[8] B. Jabber, J. Lingampalli, C.Z.n Basha, and A. Krishna, Detection of Covid-19 patients using chest X-ray images with convolution neural network and mobile net, 3rd Int. Conf. Intel Sustain Syst (ICISS) (Thoothukudi, India), IEEE, December 2020, pp. 1032–1035.
[9] Kaggle Team, Kaggle, 2022.
[10] E.B.G. Kana, M.G.Z. Kana, A.F.D. Kana, and R.H.A. Kenfack, A web-based diagnostic tool for COVID-19 using machine learning on chest radiographs (CXR), preprint, Health Informatics, April 2020.
[11] Z. Karhan and F. Akal, Covid-19 classification using deep learning in chest X-ray images, 2020 Medical Technologies Congress (TIPTEKNO) (Antalya, Turkey), IEEE, November 2020, pp. 1–4.
[12] S.H. Khan, A. Sohail, M.M. Zafar, and A. Khan, Coronavirus disease analysis using chest X-ray images and a novel deep convolutional neural network, Photodiagn. Photodyn. Therapy 35 (2021), 102473.
[13] H. Li, J. Li, X. Guan, B. Liang, Y. Lai, and X. Luo, Research on overfitting of deep learning, 15th Int. Conf. Comput. Intel. Secur. (CIS) (Macao, Macao), IEEE, December 2019, pp. 78–81.
[14] Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, A survey of convolutional neural networks: Analysis, applications, and prospects, IEEE Trans. Neural Networks Learn. Syst. 33 (2022), no. 12, 6999–7019.
[15] A. Narin, C. Kaya, and Z. Pamuk, Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks, Pattern Anal. Appl. 24 (2021), no. 3, 1207–1220.
[16] Tulin Ozturk, Muhammed Talo, Eylul Azra Yildirim, Ulas Baran Baloglu, Ozal Yildirim, and U. Rajendra Acharya, Automated detection of COVID-19 cases using deep neural networks with X-ray images, Computers in Biology and Medicine 121 (2020), 103792 (en).
[17] K. Pandey, COVID-19 killed more people in 16 months than natural disasters in 20 years, DownToEarth (2021).
[18] 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 Fractals 138 (2020), 109944.
[19] A. Rehman, M.A. Iqbal, H. Xing, and I. Ahmed, COVID-19 detection empowered with machine learning and deep learning techniques: A systematic review, Appl. Sci. 11 (2021), no. 8, 3414.
[20] A. Rehman, S. Naz, A. Khan, A. Zaib, and I. Razzak, Improving coronavirus (COVID-19) diagnosis using deep transfer learning, preprint, Infectious Diseases (except HIV/AIDS), April 2020.
[21] E. Rezende, G. Ruppert, T. Carvalho, F. Ramos, and P. de Geus, Malicious software classification using transfer learning of ResNet-50 deep neural network, 16th IEEE Int. Conf. Machine Learn. Appl. (ICMLA) (Cancun, Mexico), IEEE, December 2017, pp. 1011–1014.
[22] S. Ruuska, W. H¨am¨al¨ainen, S. Kajava, M. Mughal, P. Matilainen, and J. Mononen, Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle, Behav. Process. 148 (2018), 56–62.
[23] P.Kumar Sethy and S.K. Behera, Detection of coronavirus disease (covid-19) based on deep features, (2020), Publisher: MDPI AG.
[24] C. Shorten and T.M. Khoshgoftaar, A survey on image data augmentation for deep learning, J. Big Data 6 (2019), no. 1, 60.
[25] N. Tajbakhsh, J.Y. Shin, S.R. Gurudu, R.T. Hurst, C.B. Kendall, M.B. Gotway, and J. Liang, Convolutional neural networks for medical image analysis: Full training or fine tuning?, IEEE Trans. Med. Imag. 35 (2016), no. 5, 1299–1312.
[26] M.M. Taresh, N. Zhu, T.A.A. Ali, A.S. Hameed, and M.L. Mutar, Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks, Int. J. Biomed. Imag. 2021 (2021), 1–9.
[27] D. Theckedath and R.R. Sedamkar, Detecting Affect States Using VGG16, ResNet50 and SE-ResNet50 Networks, SN Comput. Sci. 1 (2020), no. 2, 79.
[28] F. Ucar and D. Korkmaz, COVIDiagnosis-net: Deep bayes-queezenet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images, Med. Hypoth. 140 (2020), 109761.
[29] A. Uddin, B. Talukder, M. Monirujjaman Khan, and A. Zaguia, Study on Convolutional Neural Network to Detect COVID-19 from Chest X-Rays, Math. Prob. Engin. 2021 (2021), 1–11.
[30] A. Victor Ikechukwu, S. Murali, R. Deepu, and R.C. Shivamurthy, ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images, Global Transit. Proc. 2 (2021), no. 2, 375–381.
[31] X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R.M. Summers, ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, 2017 IEEE Conf. Comput Vision Pattern Recogn (CVPR) (Honolulu, HI), IEEE, July 2017, pp. 3462–3471.
Volume 14, Issue 1
January 2023
Pages 2507-2518
  • Receive Date: 03 November 2022
  • Revise Date: 14 December 2022
  • Accept Date: 03 January 2023