Abstract
Diagnosis is a critical preventative step in Coronavirus researches. Because of the fast spread of this virus, it is necessary to present a computer-aided diagnostic (CAD) system which is very faster for radiologists. Feature Selection (FS) is a significant technique to obtain an accurate CAD system. This paper presented an effective FS model which based on wrapper approach as evaluator and Particle Swarm Optimization (PSO) as search method for classifying cases of COVID-19 using Computed Tomography (CT). This model was used PSO algorithm to identify the significant features subset within overall features set. Support Vector Machine (SVM), K-nearest neighbor (KNN) classifiers were used as evaluators with 10-fold cross validation and reached accuracy of 99.6% for SVM and 94.27% for KNN respectively. The results were shown that proposed PSO-FS model is an intelligent and outperforms other two traditional FS search methods, Genetic Algorithm (GA) and Greedy Stepwise (GS).
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Hasan, A.M., El-Kader, H.M.A., Hossam, A. (2022). Early Classification COVID-19 Based on Particle Swarm Optimization Algorithm Using CT-Images. In: Hassanien, A.E., Rizk, R.Y., Snášel, V., Abdel-Kader, R.F. (eds) The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022). AMLTA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-03918-8_52
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DOI: https://doi.org/10.1007/978-3-031-03918-8_52
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