Version 1
: Received: 23 July 2020 / Approved: 24 July 2020 / Online: 24 July 2020 (14:02:07 CEST)
How to cite:
Abdullah Farid, A.; khater, H.; selim, G. A CNN Classification Model For Diagnosis Covid19. Preprints2020, 2020070591. https://doi.org/10.20944/preprints202007.0591.v1
Abdullah Farid, A.; khater, H.; selim, G. A CNN Classification Model For Diagnosis Covid19. Preprints 2020, 2020070591. https://doi.org/10.20944/preprints202007.0591.v1
Abdullah Farid, A.; khater, H.; selim, G. A CNN Classification Model For Diagnosis Covid19. Preprints2020, 2020070591. https://doi.org/10.20944/preprints202007.0591.v1
APA Style
Abdullah Farid, A., khater, H., & selim, G. (2020). A CNN Classification Model For Diagnosis Covid19. Preprints. https://doi.org/10.20944/preprints202007.0591.v1
Chicago/Turabian Style
Abdullah Farid, A., hatem khater and gamal selim. 2020 "A CNN Classification Model For Diagnosis Covid19" Preprints. https://doi.org/10.20944/preprints202007.0591.v1
Abstract
The paper demonstrates the analysis of Corona Virus Disease based on a CNN probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed Convolution neural network structure. The Study is validated on 2002 chest X-ray images with 60 confirmed positive covid19 cases and (650 bacterial – 412 viral -880 normal) x-ray images. The proposed CNN compared with traditional classifiers with proposed CHFS feature extraction model. The experimental study has done with real data demonstrates the feasibility and potential of the proposed approach for the said cause. The result of proposed CNN structure has been successfully done to achieve 98.20% accuracy of covid19 potential cases with comparable of traditional classifiers.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
