An efficient COVID-19 detection from CT images using ensemble support vector machine with Ludo game-based swarm optimisation

Corona virus is considered as a viral disease activated from the severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) that leads to high illness and death globally. The reverse transcription polymerase chain reaction (RT-PCR) represents the standard test for the medical examination, but the test creates false negatives. In this paper, the ensemble Support vector machine with Ludo game-based swarm optimisation algorithm (ESLGSA) is proposed for the novel COVID-19 detection from the CT images and X-ray images. The proposed approach decreases the necessities of the physical labelling of the CT images and X-ray images. The proposed approach achieves the accurate infection recognition and separates the COVID-19 persons from the non-COVID-19 persons. The experimental results describes that the proposed approach enhances the accuracy value of 99.64% and the area under curve of 0.9257 in the detection of COVID-19 on CT image and X-ray image. The results described that the COVID-19 recognition using proposed approach in CT image and X-ray image is enhanced significantly than any other approaches. The results describes that the proposed approach outperforms the other approaches with respect to the measures such as accuracy, precision, recall and F-measure.