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

2021 ◽  
Vol 2021 ◽  
pp. 1-26
Author(s):  
Amin Valizadeh ◽  
Morteza Shariatee
Keyword(s):  
Neural Network ◽  
Traditional Method ◽  
Medical Image ◽  
Deep Neural Network ◽  
Sampling Methods ◽  
Semantic Segmentation ◽  
Intelligent Transportation ◽  
Segmentation Methods ◽  
Network Methods ◽  
Supervised Methods

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.

Application of convolution neural network in medical image processing

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Hongbo Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Medical Image ◽  
Sampling Methods ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Data Set ◽  
Convolution Kernels ◽  
Neural Network Structure

BACKGROUND: Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network. OBJECTIVE: This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball “yan.mat” data set, convenient to use MATLAB software for calculation. METHODSL: In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition. RESULTS: The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.

scholarly journals A Deep Neural Network for Oil Spill Semantic Segmentation in Sar Images

2018 ◽  
Author(s):  
Georgios Orfanidis ◽  
Konstantinos Ioannidis ◽  
Konstantinos Avgerinakis ◽  
Stefanos Vrochidis ◽  
Ioannis Kompatsiaris
Keyword(s):  
Neural Network ◽  
Oil Spill ◽  
Deep Neural Network ◽  
Semantic Segmentation ◽  
Sar Images

Learning deconvolutional deep neural network for high resolution medical image reconstruction

2018 ◽  
Vol 468 ◽  
pp. 142-154 ◽  
Author(s):  
Hui Liu ◽  
Jun Xu ◽  
Yan Wu ◽  
Qiang Guo ◽  
Bulat Ibragimov ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Image Reconstruction ◽  
Medical Image ◽  
Deep Neural Network ◽  
Medical Image Reconstruction

Sea Ice Classification via Deep Neural Network Semantic Segmentation

2020 ◽  
pp. 1-1
Author(s):  
Benjamin Dowden ◽  
Oscar De Silva ◽  
Weimin Huang ◽  
Dan Oldford
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Deep Neural Network ◽  
Semantic Segmentation

scholarly journals Patch-Wise Semantic Segmentation for Hyperspectral Images via a Cubic Capsule Network with EMAP Features

2021 ◽  
Vol 13 (17) ◽  
pp. 3497
Author(s):  
Le Sun ◽  
Xiangbo Song ◽  
Huxiang Guo ◽  
Guangrui Zhao ◽  
Jinwei Wang
Keyword(s):  
Neural Network ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Semantic Segmentation ◽  
Hyperspectral Images ◽  
Experimental Comparison ◽  
Spatial Features ◽  
Segmentation Methods ◽  
High Layer ◽  
Morphological Attribute

In order to overcome the disadvantages of convolution neural network (CNN) in the current hyperspectral image (HSI) classification/segmentation methods, such as the inability to recognize the rotation of spatial objects, the difficulty to capture the fine spatial features and the problem that principal component analysis (PCA) ignores some important information when it retains few components, in this paper, an HSI segmentation model based on extended multi-morphological attribute profile (EMAP) features and cubic capsule network (EMAP–Cubic-Caps) was proposed. EMAP features can effectively extract various attributes profile features of entities in HSI, and the cubic capsule neural network can effectively capture complex spatial features with more details. Firstly, EMAP algorithm is introduced to extract the morphological attribute profile features of the principal components extracted by PCA, and the EMAP feature map is used as the input of the network. Then, the spectral and spatial low-layer information of the HSI is extracted by a cubic convolution network, and the high-layer information of HSI is extracted by the capsule module, which consists of an initial capsule layer and a digital capsule layer. Through the experimental comparison on three well-known HSI datasets, the superiority of the proposed algorithm in semantic segmentation is validated.

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