Intelligent Diagnosis of Covid-19 Based on CNN-PNN
Abstract
Today the whole world suffers and fears the epidemic of the Coronavirus and the developed waves in it, as we have now reached the fourth wave, and this is a serious matter. Where the statistics of the Coronavirus in the current data showed that 213 countries are affected by this epidemic, and about 6 millions of deaths are recorded. This virus spreads rapidly, and patients mainly suffer from breathing. The patient who suffers from pre-existing health problems will be more likely to contract this disease, so there was an urgent need for artificial intelligence to enter to quickly detect this virus, so the world turned to deep learning, which is one of the most powerful methods and techniques for classification because of its use of Bayas Rule, where there is no possibility of error. This paper proposes CNN (Convolutional Neural Networks) and PNN (Proprestitc Neural Networks) mixed tomography scanning model to classify Covid-19 images, the proposed network called the CNN-PNN model. The CNN-PNN model can use CNN to compute the dependency and continuity features of the output of the middle layer of the PNN model, and correlate the properties of these middle levels with the final full network to predict the classification.
References
A. M. Alhamrouni. (2017). Iris recognition by using image preprocessing techniques,” A MASTER’S THESIS In Computer Engineering Atilim University, 2017.
Ahmed, F. R. A., Ahmed, T. E., Saeed, R. A., Alhumyani, H., Abdel-Khalek, S., & Abu-Zinadah, H. (2021). Analysis and challenges of robust E-exams performance under COVID-19. Results in Physics, 23, 103987.
Bashitialshaaer, R., Alhendawi, M., & Lassoued, Z. (2021). Obstacle comparisons to achieving distance learning and applying electronic exams during COVID-19 pandemic. Symmetry, 13(1), 99.
Bharath, S., MuraliKrishnan, K., Padmanaban, M. A., & Jenefer, B. M. (2018). An Innovative Ear Based Recognition System for Automatic Attendance Monitoring. International Journal of Latest Trends in Engineering and Technology e-ISSN, 037-042.
Dai, W. C., Zhang, H. W., Yu, J., Xu, H. J., Chen, H., Luo, S. P., ... & Lin, F. (2020). CT imaging and differential diagnosis of COVID-19. Canadian Association of Radiologists Journal, 71(2), 195-200
Elsalem, L., Al-Azzam, N., Jum'ah, A. A., & Obeidat, N. (2021). Remote E-exams during Covid-19 pandemic: A cross-sectional study of students’ preferences and academic dishonesty in faculties of medical sciences. Annals of Medicine and Surgery, 62, 326-333.
Emeršič, Ž., Štruc, V., & Peer, P. (2017). Ear recognition: More than a survey. Neurocomputing, 255, 26-39.
Eskandari, M., & Toygar, Ö. (2015). Selection of optimized features and weights on face-iris fusion using distance images. Computer Vision and Image Understanding, 137, 63-75.
Gore, D. B., Kawthekar, S. S., & Borole, V. Y. Ear Recognition Techniques for Biometrics Using Digital Image Processing: A Review.
Hamd, M. H., & Mohammed, M. Y. (2019). Multimodal biometric system based face-iris feature level fusion. Int. J. Mod. Educ. Comput. Sci, 11(5), 1-9.
https://www.kaggle.com/c/covidct/data
Huo, G., Liu, Y., Zhu, X., Dong, H., & He, F. (2015). Face–iris multimodal biometric scheme based on feature level fusion. Journal of Electronic Imaging, 24(6), 063020.
Jan, F. (2017). Non-circular iris contours localization in the visible wavelength eye images. Computers & Electrical Engineering, 62, 166-177.
Kabir, W., Ahmad, M. O., & Swamy, M. N. S. (2019). A multi-biometric system based on feature and score level fusions. IEEE Access, 7, 59437-59450.
Kandgaonkar, T. V., Mente, R. S., Shinde, A. R., & Raut, S. D. (2015). Ear biometrics: A survey on ear image databases and techniques for ear detection and recognition. IBMRD's Journal of Management & Research, 4(1), 88-103.
Karasmanaki, E., & Tsantopoulos, G. (2021). Impacts of social distancing during COVID-19 pandemic on the daily life of forestry students. Children and youth services review, 120, 105781.
Khlifi, Y., & El-Sabagh, H. A. (2017). A novel authentication scheme for E-assessments based on student behavior over e-learning platform. International Journal of Emerging Technologies in Learning, 12(4).
Mohanapriya, K., & Babu, M. M. (2017). Ear recognition by feature extraction using force field transformation. International Journal of Engineering and Computer Science, 6(3), 20742-20750.
Moser, K. M., Wei, T., & Brenner, D. (2021). Remote teaching during COVID-19: Implications from a national survey of language educators. System, 97, 102431.
Nguyen, K., Fookes, C., Ross, A., & Sridharan, S. (2017). Iris recognition with off-the-shelf CNN features: A deep learning perspective. IEEE Access, 6, 18848-18855.
Omara, I., Li, F., Zhang, H., & Zuo, W. (2016). A novel geometric feature extraction method for ear recognition. Expert Systems with Applications, 65, 127-135.
Pokhrel, S., & Chhetri, R. (2021). A literature review on impact of COVID-19 pandemic on teaching and learning. Higher Education for the Future, 8(1), 133-141.
Resmi, K. R., & Raju, G. (2019). Automatic 2D Ear Detection: A Survey. International Journal of Scientific & Technology Research, 8(11), 3643-3647.
Sameh, S. M., Moustafa, H. E. D. S., & Zaki, F. W. (2021). Feature Level Fusion of Iris and Voice. MEJ. Mansoura Engineering Journal, 40(5), 55-63.
Sharifi, O., & Eskandari, M. (2016). Optimal face-iris multimodal fusion scheme. Symmetry, 8(6), 48.
Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., ... & Yang, Y. (2021). Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE/ACM transactions on computational biology and bioinformatics, 18(6), 2775-2780.
Vijay, M., & Indumathi, G. (2021). Deep belief network-based hybrid model for multimodal biometric system for futuristic security applications. Journal of Information Security and Applications, 58, 102707.
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