WHO Based K-Means Segmentation Algorithm and Hybrid VGG19-SVM Model for Identifying COVID-19 Patients in Chest X-Ray

Abstract

COVID-19 was thought to be the most lethal and devastating disease for humans caused by the novel coronavirus currently. Accurate diagnosis may lead to earlier COVID-19 discovery and lower patient mortality, especially in instances without evident symptoms. The majority of the time, chest X-ray (CXR) images are used to diagnose this illness. Patients who are infected with coronavirus exhibit symptoms that were very similar to those of pneumonia, and the virus targets body’s respiratory organs, making breathing difficult. This paper presented a hybrid VGG19-SVM model for identifying COVID-19 patients in CXR based on wild horse optimizer (WHO) based K-means segmentation to address these problems. The proposed segmentation algorithm comprises four phases such as data gathering, pre-processing, segmentation and COVID-19 detection. CXR data were gathered from medical Internet of Things (IoT) devices. Image pre-processing was performed with the assistance of image resizing, Markov random field (MRF) and adaptive gamma correction (AGC). Then, the proposed WHO based K-clustering is used to segment the affected portion of lung CXR effectively. The hybrid classification approach is introduced based on the combination of VGG19 and SVM, which is employed to classify if the patient is in normal condition either COVID-19, pneumonia or tuberculosis. Thus, various existing methods such as VGG19, AlexNet, VGG16 and GoogleNet are taken in this analysis. The proposed VGG19-SVM attained 0.96 of F1_score, 0.97 of NPV, 0.07 FNR and 0.008 of FPR, when compared to the existing methods obtained better findings using DL techniques. This shows the effectiveness of the proposed WHO based K-means clustering algorithm and hybrid VGG19-SVM model which can be useful for segment the CXR images.