Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis

Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis

In the era of digital medicine, a vast number of medical images are produced every day. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. With the development of artificial intelligence, the algorithms of convolutional neur...

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Bibliographic Details
Main Authors: Ruixin Yang, Yingyan Yu
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-03-01
Series:Frontiers in Oncology
Subjects:
medical images
convolutional neural network
object detection
semantic segmentation
analysis
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2021.638182/full
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spelling doaj-cd4fa8dd660f4075b74987638af8e1512021-03-09T14:55:52ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-03-011110.3389/fonc.2021.638182638182Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging AnalysisRuixin YangYingyan YuIn the era of digital medicine, a vast number of medical images are produced every day. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. With the development of artificial intelligence, the algorithms of convolutional neural network (CNN) progressed rapidly. CNN and its extension algorithms play important roles on medical imaging classification, object detection, and semantic segmentation. While medical imaging classification has been widely reported, the object detection and semantic segmentation of imaging are rarely described. In this review article, we introduce the progression of object detection and semantic segmentation in medical imaging study. We also discuss how to accurately define the location and boundary of diseases.https://www.frontiersin.org/articles/10.3389/fonc.2021.638182/fullmedical imagesconvolutional neural networkobject detectionsemantic segmentationanalysis
collection DOAJ
language English
format Article
sources DOAJ
author Ruixin Yang
Yingyan Yu
spellingShingle Ruixin Yang
Yingyan Yu
Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis
Frontiers in Oncology
medical images
convolutional neural network
object detection
semantic segmentation
analysis
author_facet Ruixin Yang
Yingyan Yu
author_sort Ruixin Yang
title Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis
title_short Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis
title_full Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis
title_fullStr Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis
title_full_unstemmed Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis
title_sort artificial convolutional neural network in object detection and semantic segmentation for medical imaging analysis
publisher Frontiers Media S.A.
series Frontiers in Oncology
issn 2234-943X
publishDate 2021-03-01
description In the era of digital medicine, a vast number of medical images are produced every day. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. With the development of artificial intelligence, the algorithms of convolutional neural network (CNN) progressed rapidly. CNN and its extension algorithms play important roles on medical imaging classification, object detection, and semantic segmentation. While medical imaging classification has been widely reported, the object detection and semantic segmentation of imaging are rarely described. In this review article, we introduce the progression of object detection and semantic segmentation in medical imaging study. We also discuss how to accurately define the location and boundary of diseases.
topic medical images
convolutional neural network
object detection
semantic segmentation
analysis
url https://www.frontiersin.org/articles/10.3389/fonc.2021.638182/full
work_keys_str_mv AT ruixinyang artificialconvolutionalneuralnetworkinobjectdetectionandsemanticsegmentationformedicalimaginganalysis
AT yingyanyu artificialconvolutionalneuralnetworkinobjectdetectionandsemanticsegmentationformedicalimaginganalysis
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