Identifying Severity of COVID-19 Medical Images by Categorizing Using HSDC Model

Abstract

Since COVID-19 infections are increasing all over the world, there is a need for developing solutions for its early and accurate diagnosis is a must. Detection methods for COVID-19 include screening methods like Chest X-rays and Computed Tomography (CT) scans. More work must be done on preprocessing the datasets, such as eliminating the diaphragm portions, enhancing the image intensity, and minimizing noise. In addition to the detection of COVID-19, the severity of the infection needs to be estimated. The HSDC model is proposed to solve these problems, which will detect and classify the severity of COVID-19 from X-ray and CT-scan images. For CT-scan images, the histogram threshold of the input image is adaptively determined using the ICH Swarm Optimization Segmentation (ICHSeg) algorithm. Based on the Statistical and Shape-based feature vectors (FVs), the extracted regions are classified using a Hybrid model for CT images (HSDC-CT) algorithm. When the infections are detected, it’s classified as Normal, Moderate, and Severe. A fused FHI is formed for X-ray images by extracting the features of Histogram-oriented gradient (HOG) and Image profile (IP). The FHI features of X-ray images are classified using Hybrid Support Vector Machine (SVM) and Deep Convolutional Neural Network (DCNN) HSDC-X algorithm into COVID-19 or else Pneumonia, or Normal. Experimental results have shown that the accuracy of the HSDC model attains the highest of 94.6 for CT-scan images and 95.6 for X-ray images when compared to SVM and DCNN. This study thus significantly helps medical professionals and doctors diagnose COVID-19 infections quickly, which is the most needed in current years.