Abstract
Covid-19 pandemic changed our world as many people lost their jobs. Technology’s role is helping and facilitating our life, this role highlights situations like covid-19 virus pandemic. In this paper, we propose an innovative framework integrated with the latest machine learning techniques to assist of the covid-19 virus outbreak. The proposed framework provides three modules awareness and guidance for individuals through Chabot, diagnosis and prediction of COVID-19. The initial diagnosis for covid-19 using chest X-ray and predictions for covid-19 new cases. Moreover, the proposed framework has the potential to help citizens and national healthcare systems in curtailing the COVID-19 pandemic. The Chabot represent all the following: the symptoms, precautions and safety measures, early detection for COVID-19 cases using chest X-ray, predictions for covid-19 new cases to in light governments with the future of the pandemic. The proposed framework assist the decision makers to make improved decisions in quarantine and lock-down.
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References
American Academy of Pediatrics, David, W., Kimberlin, M.D., Michael, T., Brady, M.D., Mary Anne Jackson, M.D., Sarah, S., Long, M.D.: Committee on Infectious Diseases
Al-Abdely, H.M., Midgley, C.M., Alkhamis, A.M.: Middle east respiratory syndrome coronavirus infection dynamics and antibody responses among clinically diverse patients, Saudi Arabia. Emerg. Infect. Dis. 25(4), 753–66 (2019)
Chan, J.F., Yuan, S., Kok, K.H.: A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet 395(10223), 514–523 (2020)
Horry, M., Chakraborty, S., Paul, M., Ulhaq, A., Pradhan, B., Saha, M., Shukla, N.: X-Ray image based COVID-19 detection using pre-trained deep learning models (2020). https://doi.org/10.31224/osf.io/wx89s
Makris, A., Kontopoulos, I., Tserpes, K.: COVID-19 detection from chest X-Ray images using deep learning and convolutional neural networks (2020). https://doi.org/10.1101/2020.05.22.20110817
Apostolopoulos, I.D., Mpesiana, T.A.: COVID-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys. Eng. Sci. Med. 43(2), 635–640 (2020)
Abbas, A., Abdelsamea, M.M., Gaber, M.M.: Detrac: transfer learning of class decomposed medical images in convolutional neural networks. IEEE Access 8, 74901–74913 (2020)
Cohen, J.P., Morrison, P., Dao, L.: COVID-19 image data collection. arXiv:2003.11597 (2020)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ICLR (2015)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: The 3rd International Conference for Learning Representations, San Diego (2015)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Zhou, P., Qi, Z., Zheng, S., Xu, J., Bao, H., Xu, B.: Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling. arXiv preprint arXiv:1611.06639 (2016)
Greff, K., Srivastava, R.K., Koutnk, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2017)
Graves, A., Jaitly, N., Mohamed, A.-R.: Hybrid speech recognition with deep bidirectional LSTM. In: IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 273–278. IEEE (2013)
Cozzi, D., Albanesi, M., Cavigli, E., Moroni, C., Bindi, A., Luvarà, S., Lucarini, S., Busoni, S., Mazzoni, L., Miele, V.: Chest X-ray in new Coronavirus Disease 2019 (COVID-19) infection: findings and correlation with clinical outcome. Radiol. Med. 125, 730–737 (2020). https://doi.org/10.1007/s11547-020-01232-9
https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
https://spectrum.ieee.org/the-human-os/artificial-intelligence/medical-ai/companies-ai-coronavirus
Acknowledgement
Artificial intelligence Technology Center (AITC) at Misr University for science and Technology (MUST) would like to record our appreciation and gratitude for Academy of Scientific Research and Technology (ASRT) for their cooperation and support in this scientific research to develop an innovative deep learning framework. This research won in March 2020 among the best scientific research proposal in EGYPT from ASRT in the Apply Your Idea program.
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ElGohary, R., Hisham, A., Salama, M., Selim, Y.A.Y., Abdelwahab, M.S. (2021). A Deep Learning Solution Framework for Awareness, Diagnosing and Predicting COVID-19. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_5
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