Classification of Covid and Non-Covid Lungs CT-Scan using Deep Learning with MATLAB

matlabhelpers demonstrate how to use the MATLAB software for simulation of The novel coronavirus (nCoV) infection aroused in Wuhan, China, and has extensively spread all over the world since January 2020. World Health Organization (WHO) declared the outbreak of nCoV as a “Public Health Emergency of International Concern” on 30 January 2020 [2]. WHO named this disease as coronavirus disease (COVID-19) February 2020.

The main objective of this paper is to classify COVID-19-infected patients from chest CT images. A novel deep learning model is designed by using multi-objective differential evolution (MODE) and convolutional neural networks (CNN) for classification of human beings based upon whether they are affected from COVID-19 or not. A multi-objective fitness function is designed to classify COVID-19-infected patients by considering sensitivity and specificity. The hyperparameters of CNN are optimized by using the MODE algorithm. The proposed model is trained by considering the chest CT images of COVID-19 patients.

Abstract

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals’ precious time. Here, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (−ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

Introduction

There is a vital need to detect the disease at early stage and instantly quarantine the infected people due to unavailability of specific drugs for COVID-19. The Chinese Government reported that the diagnosis of COVID-19 is confirmed through real-time polymerase chain reaction (RT-PCR) . However, RT-PCR suffers from high false-negative rates and time-consuming . The low sensitivity of RT-PCR is not acceptable in the current epidemic situation. In some cases, the infected people may not be recognized and get suitable treatment on time. The infected people may get spread the virus to healthy people due to communicable nature of nCoV. It is observed from clinical reported of infected peoples that there is bilateral change in chest computed tomography (CT) images . Therefore, chest CT has been used as alternative tool to detect the infection caused by nCoV due to high sensitivity . The National Health Commission of China reported that chest CT can be utilized to detect the infection caused by nCoV. A large amount of pathological information can be obtained from chest CT. The radiologists are required to analyze the images of chest CT. Hence, there is a necessity to develop a deep learning-based prediction technique for analysis of chest CT without intervention of radiologist.

The main objective of this paper is to classify COVID-19-infected patients from chest CT images. A novel deep learning model is designed by using multi-objective differential evolution (MODE) and convolutional neural networks (CNN) for classification of human beings based upon whether they are affected from COVID-19 or not. A multi-objective fitness function is designed to classify COVID-19-infected patients by considering sensitivity and specificity. The hyperparameters of CNN are optimized by using the MODE algorithm. The proposed model is trained by considering the chest CT images of COVID-19 patients. The comparisons between the proposed MODE-based CNN with the competitive models such as convolutional neural networks (CNN), adaptive neuro-fuzzy inference systems (ANFIS), and artificial neural networks (ANN) are also drawn by considering the well-known classification metrics.