PSO Optimized CNN-SVM Architecture for Covid -19 Classification

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P. V. Naresh
R. Visalakshi
B. Satyanarayana


This paper presents a hybrid model that utilizes PSO particle swarm optimization, Convolution Neural Networks (CNN) and (SVM) Support Vector Machine architecture for recognition of Covid19.The planned model extracts optimized structures with particle swarm optimization then passes to Convolution Neural Network for automatic feature extraction, while the SVM serves as a Multi classifier. The dataset comprises Covid 19, Pneumonia and Normal Chest X-Ray pictures used to hone and evaluate the suggested algorithm. The most distinct traits are automatically extracted by the algorithm from these photographs. Experimental results show that the suggested framework is effective, with an average recognition accuracy of 97.42%.The most successful SVM Kernel was RBF.

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How to Cite
Naresh, P. V. ., R. Visalakshi, and B. . Satyanarayana. “PSO Optimized CNN-SVM Architecture for Covid -19 Classification”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 5s, May 2023, pp. 205-9, doi:10.17762/ijritcc.v11i5s.6646.


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