1 Affiliation : Department of Computer Engineering, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq and a PhD Student at Ain Shams University, Egypt
Email : Shaymaaa416@gmail.com
2 Affiliation : Department of Computer &Information Science, Ain Shams University, Cairo, Egypt
Email : email@example.com
COVID-19 infection is one of the most dangerous respiratory viruses, and the early detection of this disease reduces the speed of its spread among people. The goal of this virus is to infect the lung by creating patchy white shadows inside the lungs. This paper presents an intelligent method based on the deep learning technique to analyze the medical images of respiratory diseases. Two data set was used in this experiment first dataset is normal lungs taken from the Kaggle data repository. In contrast, abnormal lungs were taken from (https://github.com/muhammedtalo/COVID-19). The results show that the proposed system identifies the COVID-19 cases with an accuracy of 90%.
COVID-19; machine learning; deep learning; X-ray; Image processing
 M. A. Mohammed et al., "Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods," in IEEE Access, vol. 8, pp. 99115-99131, 2020, doi: 10.1109/ACCESS.2020.2995597.
 Zhou, Fei and Yu, Ting and Du, Ronghui and Fan, Guohui and Liu, Ying and Liu, Zhibo and Xiang, Jie andWang, Yeming and Song, Bin and Gu, Xiaoying and others, 2020. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet .
 Narin, Ali and Kaya, Ceren and Pamuk, Ziynet, 2020. Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks. arXiv preprint arXiv:2003.10849 .
 Xu, Zhe and Shi, Lei and Wang, Yijin and Zhang, Jiyuan and Huang, Lei and Zhang, Chao and Liu, Shuhong and Zhao, Peng and Liu, Hongxia and Zhu, Li and others, 2020. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. The Lancet respiratory medicine .
 Tulin Ozturk , Muhammed Talo , Eylul Azra Yildirim , Ulas Baran Baloglu , Ozal Yildirim , U. Rajendra Acharya ," Automated detection of COVID-19 cases using deep neural networks with X-ray images" Computers in Biology and Medicine ,2020 , https://doi.org/10.1016/j.compbiomed.2020.103792
 Mohamed Amine Ferrag, Leandros Maglaras, Sotiris Moschoyiannis, and Helge Janicke , Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study , Journal of Information Security and Applications vol 50 , 2020
 L. J. Muhammad Md ,Milon Islam , Sani Sharif Usman , Safial Islam Ayon ," Predictive Data Mining Models for Novel Coronavirus (COVID‑19) Infected Patients’ Recovery" SN Computer Science , 2020
 Shuai Wang, Bo Kang , Jinlu Ma , Xianjun Zeng5, Mingming Xiao1, Jia Guo , Mengjiao Cai, Jingyi Yang, Yaodong Li, Xiangfei Meng2 , Bo Xu1," A deep learning algorithm using CT images to screen for Corona Virus Disease(COVID-19) , 2020
 Cohen JP (2020) COVID-19 image data collection. https://github.com/ieee8023/COVID-chestxray-dataset.
 Basu, Mitra ," Gaussian-based edge-detection methods-a survey" vol 32 ,number 3 , pp 252-260 , 2002
 Mohammed, M.A., Abdulkareem, K.H., Mostafa, S.A., Ghani, M.K.A., Maashi, M.S., Garcia-Zapirain, B., Oleagordia, I., Alhakami, H. and AL-Dhief, F.T., 2020. Voice Pathology Detection and Classification Using Convolutional Neural Network Model. Applied Sciences, 10(11), p.3723.
 Obaid, O.I., Mohammed, M.A., Ghani, M.K.A., Mostafa, A. and Taha, F., 2018. Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer. International Journal of Engineering & Technology, 7(4.36), pp.160-166.
 Mohammed, M.A., Abd Ghani, M.K., Arunkumar, N.A., Mostafa, S.A., Abdullah, M.K. and Burhanuddin, M.A., 2018. Trainable model for segmenting and identifying Nasopharyngeal carcinoma. Computers & Electrical Engineering, 71, pp.372-387.
 Singh Anuj , Kumar Gupta, Bhupendra, " A novel approach for breast cancer detection and segmentation in a mammogram " Procedia Computer Science, vol 54 , pp 676-682, 2015 .
 Shaymaa Adnan Abdulrahman , Wael Khalifa , Mohamed Roushdy , Abdel Badeeh M. Salem , "Comparative study for 8 computational intelligence algorithms for human identification " 2020 ,https://doi.org/10.1016/j.cosrev.2020.100237
 Zebari, D.A., Haron, H., Zeebaree, S.R. and Zeebaree, D.Q., 2019, April. Enhance the Mammogram Images for Both Segmentation and Feature Extraction Using Wavelet Transform. In 2019 International Conference on Advanced Science and Engineering (ICOASE) (pp. 100-105). IEEE.
 Coates A, Ng AY (2011) Selecting receptive fields in deep networks. Adv Neural Inf Process Syst, 2528–2536
 Arunkumar, N., Mohammed, M.A., Mostafa, S.A., Ibrahim, D.A., Rodrigues, J.J. and de Albuquerque, V.H.C., 2020. Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks. Concurrency and Computation: Practice and Experience, 32(1), p.e4962.
 Khatami Amin , Khosravi, Abbas , Lim, Chee Peng , Nahavandi, Saeid " A wavelet deep belief network-based classifier for medical images, International Conference on Neural Information Processing, pp467-474 , 2016
Wang, Linda and Wong, Alexander, 2020. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images. arXiv preprint arXiv:2003.09871
 Abd Ghani, M.K., Mohammed, M.A., Arunkumar, N., Mostafa, S.A., Ibrahim, D.A., Abdullah, M.K., Jaber, M.M., Abdulhay, E., Ramirez-Gonzalez, G. and Burhanuddin, M.A., 2020. Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques. Neural Computing and Applications, 32(3), pp.625-638.
 Mohammed, M.A., Al-Khateeb, B., Rashid, A.N., Ibrahim, D.A., Abd Ghani, M.K. and Mostafa, S.A., 2018. Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images. Computers & Electrical Engineering, 70, pp.871-882.
 Mohammed, M.A., Abd Ghani, M.K., Hamed, R.I. and Ibrahim, D.A., 2017. Analysis of an electronic methods for nasopharyngeal carcinoma: Prevalence, diagnosis, challenges and technologies. Journal of Computational Science, 21, pp.241-254.
 Arunkumar, N., Mohammed, M.A., Abd Ghani, M.K., Ibrahim, D.A., Abdulhay, E., Ramirez-Gonzalez, G. and de Albuquerque, V.H.C., 2019. K-means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Computing, 23(19), pp.9083-9096.
 Abdulkareem, K.H., Mohammed, M.A., Gunasekaran, S.S., Al-Mhiqani, M.N., Mutlag, A.A., Mostafa, S.A., Ali, N.S. and Ibrahim, D.A., 2019. A review of Fog computing and machine learning: Concepts, applications, challenges, and open issues. IEEE Access, 7, pp.153123-153140.