%0 Journal Article %1 Abdullah_Farid_2020 %A Farid, Ahmed Abdullah %A Selim, Gamal Ibrahim %A Khater, Hatem Awad A. %D 2020 %I MDPI AG %J IJSER %K COVID19 CT-image anovel diagnse learning machine to used %R 10.20944/preprints202003.0284.v1 %T A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19) %U https://doi.org/10.20944%2Fpreprints202003.0284.v1 %V 11 %X The paper demonstrates the analysis of Corona Virus Disease based on a 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 approach for feature extraction. The combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system(SHC). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

@article{Abdullah_Farid_2020, abstract = {The paper demonstrates the analysis of Corona Virus Disease based on a 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 approach for feature extraction. The combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system(SHC). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.}, added-at = {2020-03-24T08:17:23.000+0100}, author = {Farid, Ahmed Abdullah and Selim, Gamal Ibrahim and Khater, Hatem Awad A.}, biburl = {https://www.bibsonomy.org/bibtex/2f071408436690efd265d32e4ff3ca800/ahmed.abdullah}, description = {A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19)}, doi = {10.20944/preprints202003.0284.v1}, interhash = {a5ad54428a62abd32c90a59e8be9a3d5}, intrahash = {f071408436690efd265d32e4ff3ca800}, issn = {2229-5518}, journal = {IJSER}, keywords = {COVID19 CT-image anovel diagnse learning machine to used}, month = mar, publisher = {{MDPI} {AG}}, timestamp = {2020-03-24T08:17:23.000+0100}, title = {A Novel Approach of {CT} Images Feature Analysis and Prediction to Screen for Corona Virus Disease ({COVID}-19)}, url = {https://doi.org/10.20944%2Fpreprints202003.0284.v1}, volume = 11, year = 2020 }