Preprint Article Version 1 This version is not peer-reviewed

A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19)

Version 1 : Received: 16 March 2020 / Approved: 18 March 2020 / Online: 18 March 2020 (08:32:46 CET)

A peer-reviewed article of this Preprint also exists.

Farid, A. A., Selim, G. I., Awad, H., & Khater, A. (2020). A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19). International Journal of Scientific & Engineering Research, 11(3). http://dx.doi.org/10.14299/ijser.2020.03.02 Farid, A. A., Selim, G. I., Awad, H., & Khater, A. (2020). A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19). International Journal of Scientific & Engineering Research, 11(3). http://dx.doi.org/10.14299/ijser.2020.03.02

Journal reference: International Journal of Scientific & Engineering Research 2020, 11, 82
DOI: 10.14299/ijser.2020.03.02

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.

Subject Areas

deep learning; composite hybrid feature selection; machine learning; stack ‎hybrid classification; CT-image; MPEG7 edge histogram feature extraction; CNN

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