Abstract
Viruses and bacteria are now much more dangerous than ever because of their ability to develop their immune systems against medicines. COVID-19 spread rapidly over the entire world. Intelligent systems are needed to help doctors determine whether there is an infection or not. Convolution Neural Network is a class of deep learning which can analyze visual images. This paper investigates Convolution Neural Network CNN’s effectiveness in recognizing the infected lung images with COVID-19 or any other diseases. The proposed CNN model classifies the lung X-ray into three categories COVID-19, pneumonia, and normal. The model was applied using Cohen and collected databases from Kaggle and GitHub. The proposed model achieved an average accuracy of 96.8% to classify between COVID-19, pneumonia, and normal lungs x-rays, and 100% accuracy to classify COVID-19 versus normal lungs X-rays images.
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References
World Health Organization. Emergencies preparedness, response, disease outbreak newsWorld Health Organization (WHO). Pneumonia of unknown cause–China (2020)
Fan, W., Zhao, S., Bin, Y.: A new coronavirus associated with human respiratory disease in China. Nature 579(7798), 265–269 (2020)
Zu, Z.Y., Jiang, M.D., Xu, P.P., Chen, W., Ni, Q.Q., Lu, G.M.: Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology, February 2020 https://doi.org/10.1148/radiol.2020200490
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Talo, M., Yildirim, O., Baloglu, U.B., Aydin, G., Acharya, U.R.: Convolutional neural networks for multi-class brain disease detection using MRI images. 78 (2019)
Celik, Y., Talo, M., Yildirim, O., Karabatak, M., Acharya, U.R.: Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. Pattern Recogn. Lett. 133, 232–239 (2020)
Esteva, A., Kuprel, B., Novoa, R.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)
Yoon, S.H., Lee, K.H.: Chest radiographic and CT findings of the 2019 novel coronavirus disease (COVID-19): analysis of nine patients treated in Korea. Korean J. Radiol. 21, 494–500 (2020)
Rajpurkar, P., Irvin, J., et al.: Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv:1711.05225, vol. 3 (2017)
Tan, J., Fujita, H., Sivaprasad, S., Bhandary, S., Rao, A.K., Chua, K., Acharya, U.R.: Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Inf. Sci. 420, 66–76 (2017)
Gaal, G., Maga, B., Lukacs, A.: Attention U-net based adversarial architectures for chest x-ray lung segmentation. arXiv:2003.10304, vol. 1 (2020)
Thevenot, J., Lopez, M.B., Hadid, A.: A survey on computer vision for assistive medical diagnosis from faces. IEEE J. Biomed. Health Inform. 22(5), 1497–1511 (2018)
Islam, M.M., Iqbal, H., Haque, M.R., Hasan, M.K.: Prediction of breast cancer using support vector machine and K-Nearest neighbors. In: 2017 IEEE Region 10 Humanitarian Technology Conference, pp. 226–229 (2017)
Haque, M.R., Islam, M.M., Iqbal, H., Reza, M.S., Hasan, M.K.: Performance evaluation of random forests and artificial neural networks for the classification of liver disorder. In: International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshah, pp. 1–5 (2018)
Hasan, M.K., Islam, M.M., Hashem, M.M.A.: Mathematical model development to detect breast cancer using multigene genetic programming. In: 5th International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, pp. 574–579 (2016)
Islam Ayon, S., Milon Islam, M.: Diabetes prediction: a deep learning approach. Int. J. Inf. Eng. Electron. Bus. 11–21 (2019)
Ayon, S.I., Islam, M.M., Hossain, M.R.: Coronary artery heart disease prediction: a comparative study of computational intelligence techniques. IETE J. Res. 1–20 (2020)
Jiang, X.: Feature extraction for image recognition and computer vision. In: 2nd IEEE International Conference on Computer Science and Information Technology, Beijing, vol. 2009, pp. 1–15 (2009)
Wang, L., Lin, Z.Q., Wong, A.: COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest x-ray images. arXiv, March 2020
Hemdan, E.E., Shouman, M.A., Karar, M.E.: Covidx-net: a framework of deep classifiers to diagnose covid-19 in X-ray images. arXiv:2003.11055, vol. 1, March 2020
Kumar, P., Kumari, S.: Detection of coronavirus disease (COVID-19) based on deep features. preprints.org, p. 9, March 2020
Ioannis, D., Apostolopoulos, T.B.: COVID-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. [PMC free article] [PubMed] (2020)
Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., et al.: Deep learning enables accurate diagnosis of novel Coronavirus (COVID-19) with CT images. MedRxiv (2020)
Khan, A.I., Shah, J.L., Bhat, M.M.: CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest X-ray images. Comput. Methods Programs Biomed. (2020)
Narin, A., Kaya, C., Pamuk, Z.: Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. arXiv:2003.10849
Basu, S., Mitra, S., Saha, N.: Deep learning for screening COVID-19 using chest X-ray images. arXiv:2004.10507 (2020)
Bejoy, A., Nair, M.S.: Computer-aided detection of covid-19 from x-ray images using multi-CNN and Bayes net classifier. Biocybern. Biomed. Eng. 40(4), 1436–1445 (2020)
Cohen, J.P., Morrison, P., Dao, L.: Covid-19 image data collection. arXiv:2003.11597 (2020). https://github.com/ieee8023/covid-chestxray-dataset
Kermany, D.S., Goldbaum, M., Cai, W., Valentim, C.C., Liang, H., Baxter, S.L., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–32 (2020)
Mooney, P.: Pneumonia X rays (2018). https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. Accessed 14 July 2020
COVID-19 chest X-ray. https://github.com/agchung. Accessed 20 June 2020
Ebied, H.M.: Feature extraction using PCA and Kernel-PCA for face recognition. In: 8th International Conference on Informatics and Systems (INFOS), Cairo, pp. MM-72–MM-77 (2012)
Ghaleb, M.S., Ebied, H.M., Shedeed, H.A., Tolba, M.F.: Image retrieval based on self-organizing feature map and multilayer perceptron neural networks classifier. In: Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt, pp. 189–193 (2019)
Shafaey, M.A., Salem, M.A.M., Ebied, H.M., Al-Berry, M.N., Tolba, M.F.: Deep learning for satellite image classification. In: The International Conference on Advanced Intelligent Systems and Informatics, vol. 845. Springer (2018)
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Ghaleb, M.S., Ebied, H.M., Shedeed, H.A., Tolba, M.F. (2021). COVID-19 X-rays Model Detection Using Convolution Neural Network. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_1
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