Diagnosis of COVID-19 patients by adapting hyper parametertuned deep belief network using hosted cuckoo optimization algorithm

Abstract

ABSTRACT COVID-19 is an infection caused by recently discovered corona virus. The symptoms of COVID-19 are fever, cough and dumpiness of breathing. A quick and accurate identification is essential for an efficient fight against COVID-19. A machine learning technique is initiated for categorizing the chest x-ray images into two cases: COVID-19 positive case or negative case. In this manuscript, the categorization of COVID-19 can be determined by hyper parameter tuned deep belief network using hosted cuckoo optimization algorithm. At first, the input chest x-ray images are pre-processed for removing noises. In this manuscript, the deep belief network method is enhanced by hosted cuckoo optimization approach for getting optimum hyper tuning parameters. By this, exact categorization of COVID-19 is attained effectively. The proposed methodology is stimulated at MATLAB. The proposed approach attains 28.3% and 23.5% higher accuracy for Normal and 32.3% and 31.5% higher accuracy for COVID-19, 19.3% and 28.5% higher precision for Normal and 45.3% and 28.5% higher precision for COVID-19, 20.3% and 21.5% higher F-score for Normal and 40.3% and 21.5% higher F-score for COVID-19. The proposed methodology is analyzed using two existing methodologies, as Convolutional Neural Network with Social Mimic Optimization (CNN-SMO) and Support Vector Machine classifier using Bayesian Optimization algorithm (SVM-BOA).