DOI: https://doi.org/10.18517/ijods.2.1.9-18.2021

COVID-CNNnet: Convolutional Neural Network for Coronavirus Detection

Ali A. Alani (1) , Ahmed A. Alani (2) , Khudhair A.M. Abed AL Ani (3)
(1) Department of Computer Science, University of Diyala, Diyala, Iraq
(2) Baquba Technical Institute, Middle Technical University, Diyala, Iraq
(3) Physiology and Pharmacology Dept., College of Veterinary Medicine, Diyala University, Iraq
Fulltext View | Download

Abstract

The coronavirus disease (COVID-19) is the most recent severe diseases that has spread globally at an exponential rate. During this crisis, any technological approach that allows highly precise early detection of COVID-19 infection will save many lives. The main clinical technique for COVID-19 recognition is the reverse transcription polymerase chain reaction (RT-PCR). However, the RT-PCR testing tool is time-consuming, inaccurate and requires skilled medical staff. Therefore, auxiliary diagnostic tools should be developed to stop the spread of COVID-19 amongst people. Chest X-ray imaging is a readily available method that able to serve as an extremely good alternative for RT-PCR in identifying patients with COVID-19 diseases because it provides salient COVID-19 virus information. In this study, the COVID-CNNnet model proposed based on a convolutional neural network (CNN) deep learning (DL) algorithm, to detect COVID-19 cases rapidly and accurately based on patient chest X-ray images. The proposed COVID-CNNnet model aims to provide an accurate binary diagnostic classification for COVID-19 cases versus normal cases. To validate the proposed model, 3540 chest X-ray images were obtained from multiple sources, including 1770 images for COVID-19 cases. Results show that the COVID-CNNnet model can identify all classes (COVID-19 cases versus normal cases) with an accuracy of 99.86%. The proposed method can assist doctors diagnose COVID-19 cases effectively using chest X-ray images.

Article Details

How to Cite
[1]
A. A. Alani, A. A. Alani, and K. A. Abed AL Ani, “COVID-CNNnet: Convolutional Neural Network for Coronavirus Detection”, Int. J. Data. Science., vol. 2, no. 1, pp. 9-18, Apr. 2021.
Section
Articles

References

Apostolopoulos, Ioannis D., and Tzani A. Mpesiana. "Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks." Physical and Engineering Sciences in Medicine 43.2 (2020): 635-640.

Wu, Zunyou, and Jennifer M. McGoogan. "Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention." Jama 323.13 (2020): 1239-1242.

World Health Organization’s, “About worldometer COVID-19 data-Worldometer,” 2020. [Online]. Available: https://www.worldometers.info/coronavirus/.

Sethy, P. K., and S. K. Behera. "Detection of coronavirus disease (COVID-19) based on deep features. Preprints." Preprint posted online March 19 (2020).

Al-Karawi, Dhurgham, et al. "Ai based chest x-ray (cxr) scan texture analysis algorithm for digital test of covid-19 patients." medRxiv (2020).

Narin, Ali, Ceren Kaya, and Ziynet Pamuk. "Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks." arXiv preprint arXiv:2003.10849 (2020).

Hassanien, Aboul Ella, et al. "Automatic x-ray covid-19 lung image classification system based on multi-level thresholding and support vector machine." medRxiv (2020).

Jiang, Fang, et al. "Review of the clinical characteristics of coronavirus disease 2019 (COVID-19)." Journal of general internal medicine 35.5 (2020): 1545-1549.

Ucar, Ferhat, and Deniz Korkmaz. "COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images." Medical Hypotheses 140 (2020): 109761.

Gaál, Gusztáv, Balázs Maga, and András Lukács. "Attention u-net based adversarial architectures for chest x-ray lung segmentation." arXiv preprint arXiv:2003.10304 (2020).

Talo, Muhammed, et al. "Convolutional neural networks for multi-class brain disease detection using MRI images." Computerized Medical Imaging and Graphics 78 (2019): 101673.

Esteva, Andre, et al. "Dermatologist-level classification of skin cancer with deep neural networks." nature 542.7639 (2017): 115-118.

Ozturk, Tulin, et al. "Automated detection of COVID-19 cases using deep neural networks with X-ray images." Computers in biology and medicine 121 (2020): 103792.

Wang, Linda, Zhong Qiu Lin, and Alexander Wong. "Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images." Scientific Reports 10.1 (2020): 1-12.

Afshar, Parnian, et al. "Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images." Pattern Recognition Letters 138 (2020): 638-643.

Sahinbas, Kevser, and Ferhat Ozgur Catak. "Transfer learning based convolutional neural network for covid-19 detection with x-ray images." Data Science for COVID-19, Computational Perspectives 24(2020): 1-24.

Rahimzadeh, Mohammad, and Abolfazl Attar. "A new modified deep convolutional neural network for detecting COVID-19 from X-ray images." arXiv preprint arXiv:2004.08052 (2020).

Cohen, Joseph Paul, et al. "Covid-19 image data collection: Prospective predictions are the future." arXiv preprint arXiv:2006.11988 (2020).

“COVID-19 Chest X-ray,” 2020. [Online]. Available: https://github.com/agchung/Figure1-COVID-chestxray-dataset. [Accessed: 10-Nov-2020].

P. Mooney, “Chest X-ray Images (Pneumonia).,” 2020. [Online]. Available: https://www.kaggle.com/%0Apaultimothymooney/chest-xray-pneumonia/. [Accessed: 10-Nov-2020].

Taherkhani, Aboozar, et al. "Activity recognition from multi-modal sensor data using a deep convolutional neural network." Science and Information Conference. Springer, Cham, 2018.

S. Salesi, A.A. Alani, and G. Cosma, “A Hybrid Model for Classification of Biomedical Data using Feature Filtering and a Convolutional Neural Network,” 2018 Fifth Int. Conf. Soc. Networks Anal. Manag. Secur., pp. 226–232, 2018.

A. A. Alani and G. Cosma, “Hand Gesture Recognition Using an Adapted Convolutional Neural Network with Data Augmentation,” in 2018 4th International Conference on Information Management (ICIM), 2018, pp. 5–12.

B. Pandya, G. Cosma, and A. A. Alani, “Fingerprint Classification using a Deep Convolutional Neural Network,” in 2018 4th International Conference on Information Management (ICIM), 2018, pp. 86–91.

A. A. Alani, G. Cosma, and A. Taherkhani, “Classifying Imbalanced Multi-modal Sensor Data for Human Activity Recognition in a Smart Home using Deep Learning,” in 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1–8.

Hemdan, Ezz El-Din, Marwa A. Shouman, and Mohamed Esmail Karar. "Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images." arXiv preprint arXiv:2003.11055 (2020).