Seg-Net: Automatic Lung Infection Segmentation of COVID-19 from CT images

Abstract

COVID-19 is a deadly disease which causes infection in both animals and human beings. It is a zoonotic disease that scatters worldwide in the beginning of the year 2020. COVID-19 is termed as Coronavirus Disease 2019 that makes the whole world to suffer from this existential infection. The lung contamination is found automatically by chest Computed Tomography images that help to tackle COVID-19. During the separation of the diseased portion from the X-ray slices, it produces lots of demands which include huge difference in the disease attribute and low intensity difference in the middle of infected tissue and usual tissues. The collection of huge quantity of information is impossible in a short period of time and pedagogy of the deep model. For overcoming the Lung disease separation of COVID-19 by using S eg-Net is suggested to analyze the affected portions automatically from chest X-ray scan. Here, Convolutional Neural Network (CNN) architecture for semantic pixel-wise segmentation named as Semantic Network is utilized. S eg-Net segmentation is a core trainable engine that contains an encoder network and also a corresponding decoder network that is continued by a pixel-wise classification layer. The structure of the encoder network is physiographic and it is equal with the 13 convolutional layers in the Visual Geometry Group 16 network. The originality of the semantic network is located in this method of decoder up samples with the lower resolution input map features. Exactly, the pooling was applied by the decoder that indicates max pooling process in the corresponding encoder for behaving like the non-linear up sampling. Comprehensive observation in COVID-19 real CT volumes and the SemiSeg are determined and it is suggested that the Semantic network performs the cut-ting edge segmentation models, and then it promotes the state in the art presentation.