COVID-19 Severity Prediction Using Enhanced Whale with Salp Swarm Feature Classification

Abstract

Computerized tomography (CT) scans and X-rays play an important role in the diagnosis of COVID-19 and pneumonia. On the basis of the image analysis results of chest CT and X-rays, the severity of lung infection is monitored using a tool. Many researchers have done in diagnosis of lung infection in an accurate and efficient takes lot of time and inefficient. To overcome these issues, our proposed study implements four cascaded stages. First, for pre-processing, a mean filter is used. Second, texture feature extraction uses principal component analysis (PCA). Third, a modified whale optimization algorithm is used (MWOA) for a feature selection algorithm. The severity of lung infection is detected on the basis of age group. Fourth, image classification is done by using the proposed MWOA with the salp swarm algorithm (MWOA-SSA). MWOA-SSA has an accuracy of 97%, whereas PCA and MWOA have accuracies of 81% and 86%. The sensitivity rate of the MWOA-SSA algorithm is better that of than PCA (84.4%) and MWOA (95.2%). MWOA-SSA outperforms other algorithms with a specificity of 97.8%. This proposed method improves the effective classification of lung affected images from large datasets.