The Progress of Medical Image Semantic Segmentation Methods for Application in COVID-19 Detection.

Amin Valizadeh,Morteza Shariatee,Suresh Manic

doi:10.1155/2021/7265644

Amin Valizadeh, Morteza Shariatee + Show 1 more

Open Access

https://doi.org/10.1155/2021/7265644

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Abstract

Image medical semantic segmentation has been employed in various areas, including medical imaging, computer vision, and intelligent transportation. In this study, the method of semantic segmenting images is split into two sections: the method of the deep neural network and previous traditional method. The traditional method and the published dataset for segmentation are reviewed in the first step. The presented aspects, including all-convolution network, sampling methods, FCN connector with CRF methods, extended convolutional neural network methods, improvements in network structure, pyramid methods, multistage and multifeature methods, supervised methods, semiregulatory methods, and nonregulatory methods, are then thoroughly explored in current methods based on the deep neural network. Finally, a general conclusion on the use of developed advances based on deep neural network concepts in semantic segmentation is presented.

Highlights

Semantic segmentation of medical images is known as pixel-level classification. e task is to cluster the parts of the image side by side, which belong to a class of similar objects [1]. e other two key functions of the image are to classify the image’s surface and define it
Traditional methods are called the methods that came before the deep neural network. e following parts of this convention are followed in this study and standard segmentation techniques are briefly analyzed in this article, and, most significantly, this development builds on the recent progress of adopting and organizing a deep neural network from different aspects
Complexity e artificial intelligence (AI)-based analysis is rapidly evolving in the 2D and 3D DL diagnosis of models were used coronavirus, and the detection is being made with great accuracy

Summary

Introduction

Semantic segmentation of medical images is known as pixel-level classification. e task is to cluster the parts of the image side by side, which belong to a class of similar objects [1]. e other two key functions of the image are to classify the image’s surface and define it. If the software significantly increases the risk of developing the disease, the case will be reviewed by a radiologist or a physician for Computational Intelligence and Neuroscience further treatment/quarantine. Such systems or their modifications, after validation and testing, can be a key factor in the diagnosis and control of patients with the virus [13]. E following parts of this convention are followed in this study and standard segmentation techniques are briefly analyzed in this article, and, most significantly, this development builds on the recent progress of adopting and organizing a deep neural network from different aspects.

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