Automatic Detection of Covid-19 in CT Images Using an Optimized Deep Neural Network Classifier

Abstract

An exact and quick finding of Covid-19 patients plays a significant part in the initial period of medicinal treatment and prevention. Automatic recognition of COVID-19 cases using CT images may help lessen the effect of this infection on human civilization. In recent years, computer vision is the major solution for diagnosing the covid-19 disease by using CT images. Besides, many researchers had presented efficient artificial intelligence techniques for identifying Covid-19 disease. Nevertheless, the accuracy and time consumption of the model further to be improved. Thus, in this work, we propose a Covid-19 disease classification using a hybrid deep neural network with handcrafted features. The proposed approach consists of three stages namely, pre-processing, handcrafted feature extraction, and classification. Initially, the images are given to the pre-processing stage to remove the noise present in the input images. Then, we extract the hand-crafted features (Gray-Level Co-Occurrence Matrix (GLCM)) from each image. After that, the pre-processed image is given to the input of the optimized deep neural network classifier to classify an image as normal or abnormal. The proposed optimized deep neural network is a combination of a convolution neural network (CNN) and adaptive artificial jelly optimization (A2JO) algorithm. To enhance the performance of classifier accuracy, the extracted handcrafted features are fused with the hybrid deep neural network. The performance of the proposed approach was analyzed based on different metrics and performance compared with other techniques.