Hybrid ICHO-HSDC Model For Accurate Covid-19 Detection and Classification From CT Scan And X-Ray Images

Abstract

The worldwide demand for medical care has increased due to the increasing expansion of Covid-19 cases. Therefore, in this case, prompt and precise identification of this illness is crucial. Health professionals are using additional screening techniques including CT imaging as well as chest Xrays for this. Pre-processing the CT scan pictures to eliminate the areas of areas, normalize image contrast, and minimize image noise, however, receives little attention. The seriousness of the Covid- 19 infection must be assessed in addition to the Covid-19 detection and categorization. An ICHOHYBRID model for Covid-19 identification and classification from X-ray, as well as CT scan images, is offered as a solution to these issues. Histogram and morphological image processing methods are used for CT-scan images. The Improved Chicken Swarm Optimization (ICHO) technique is used to find the input image’s histogram threshold. The extracted areas are categorized using the Convolutional Neural Network method based on a feature vector. When infections are found, the CNN algorithm is used to categorize them as severe, moderate, or extremely severe using Support Vector Machine. To eliminate the noise from the test pictures for X-ray imaging, the Adapted Anisotropic Diffusion Filtering (A2DF) approach is used. Once the preprocessing is completed, features are extracted using an Image profile (IP) and Histogram-oriented gradient (HOG) to create a fused HOG and IP feature. Using the HYBRID method, the FHI characteristics are divided into 3 classes. When compared to SVM and CNN, the study provides the best accuracy, with scores of 94.6 for CT scan pictures and 95.6 for X-ray images.