Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

Abstract

The novel coronavirus infection (COVID-19) that was first identified in China in December 2019 has spread across the globe rapidly infecting over ten million people. The World Health Organization (WHO) declared it as a pandemic on March 11, 2020. What makes it even more critical is the lack of vaccines available to control the disease, although many pharmaceutical companies and research institutions all over the world are working toward developing effective solutions to battle this life-threatening disease. X-ray and computed tomography (CT) images scanning is one of the most encouraging exploration zones; it can help in finding and providing early diagnosis to diseases and gives both quick and precise outcomes. In this study, convolution neural networks method is used for binary classification pneumonia-based conversion of VGG-19, Inception_V2 and decision tree model on X-ray and CT scan images dataset, which contains 360 images. It can infer that fine-tuned version VGG-19, Inception_V2 and decision tree model show highly satisfactory performance with a rate of increase in training and validation accuracy (91%) other than Inception_V2 (78%) and decision tree (60%) models. Keywords COVID-19 Á X-ray images Á CT scan Á CNN Á VGG-16 Á Inception_V2 Á Decision tree 1 Introduction The first case of the virus became exposed in Wuhan city of China in November 2019, there are 1,100,000 peoples living in this city and it interfaces numerous urban communities of China. The outbreak of atypical and individual-to-individual transmissible pneumonia brought about by the severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) has caused a worldwide. There have been in excess of 26,000,000 confirmed cases of the corona virus disease (COVID-19) on the globe, as of April 23, 2020. As indicated by the WHO, 16-21% of individuals with the infection have gotten seriously sick with a Communicated by Valentina E. Balas.

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“…for building the prediction models based on various risk factors. These type of approaches were provided the effective results in the previous researches (Yi et al 2018 ; Castillo and Melin 2020 ; Dansana et al 2020 ; Kannan et al 2020 ; Melin et al 2020a , b ). The compulsory input (on the linguistic scale) from all decision-makers may be gathered independently for optimum weight computation of the risk factors using BWM.…”

“…Castillo and Melin ( 2020 ) proposed a hybrid intelligent approach for forecasting the future trends of pandemic situations based on the COVID-19 time series of confirmed cases and deaths. Dansana et al ( 2020 ) used deep learning algorithms to map the computed tomography and X-rays reports of COVID-19 patients for providing better and faster treatment. Melin et al ( 2020a ) done a spatial evolution of different country maps for exploring COVID-19 pandemic situations by using an unsupervised neural network.…”

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“…There are also studies in the literature using pre-trained deep neural networks to detect COVID-19 from X-ray images (Hemdan et al 2003;Narin et al 2003). Dansana et al (2020) used convolution neural networks (CNN) for binary classification pneumonia-based conversion of VGG-19, Inception V2 and decision tree model on X-ray and CT scan image datasets. In this study, automatic COVID-19 is detected from chest X-ray images using capsule networks.…”

IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification

“…In addition, maximum performance is attained by a manual intervention of CT images. Thus, Artificial intelligence (AI) related results are the inexpensive and exact diagnosis for COVID-19 and many other diseases [ 22 , 23 ]. As an inclusion, Deep learning (DL) and AI methodologies are applied in biomedical applications.…”