A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network

Tumors are new tissues that are harmful to human health. The malignant tumor is one of the main diseases that seriously affect human health and threaten human life. For cancer treatment, early detection of pathological features is essential to reduce cancer mortality effectively. Traditional diagnos...

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Autores principales:	Yan Yan, Xu-Jing Yao, Shui-Hua Wang, Yu-Dong Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	tumor detection convolutional neural network application of tumor detection traditional tumor detection methods computer-aided diagnosis Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/bb865d24022b4a55a831fadb6e5dd0f2
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id	oai:doaj.org-article:bb865d24022b4a55a831fadb6e5dd0f2
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spelling	oai:doaj.org-article:bb865d24022b4a55a831fadb6e5dd0f22021-11-25T16:46:51ZA Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network10.3390/biology101110842079-7737https://doaj.org/article/bb865d24022b4a55a831fadb6e5dd0f22021-10-01T00:00:00Zhttps://www.mdpi.com/2079-7737/10/11/1084https://doaj.org/toc/2079-7737Tumors are new tissues that are harmful to human health. The malignant tumor is one of the main diseases that seriously affect human health and threaten human life. For cancer treatment, early detection of pathological features is essential to reduce cancer mortality effectively. Traditional diagnostic methods include routine laboratory tests of the patient’s secretions, and serum, immune and genetic tests. At present, the commonly used clinical imaging examinations include X-ray, CT, MRI, SPECT scan, etc. With the emergence of new problems of radiation noise reduction, medical image noise reduction technology is more and more investigated by researchers. At the same time, doctors often need to rely on clinical experience and academic background knowledge in the follow-up diagnosis of lesions. However, it is challenging to promote clinical diagnosis technology. Therefore, due to the medical needs, research on medical imaging technology and computer-aided diagnosis appears. The advantages of a convolutional neural network in tumor diagnosis are increasingly obvious. The research on computer-aided diagnosis based on medical images of tumors has become a sharper focus in the industry. Neural networks have been commonly used to research intelligent methods to assist medical image diagnosis and have made significant progress. This paper introduces the traditional methods of computer-aided diagnosis of tumors. It introduces the segmentation and classification of tumor images as well as the diagnosis methods based on CNN to help doctors determine tumors. It provides a reference for developing a CNN computer-aided system based on tumor detection research in the future.Yan YanXu-Jing YaoShui-Hua WangYu-Dong ZhangMDPI AGarticletumor detectionconvolutional neural networkapplication of tumor detectiontraditional tumor detection methodscomputer-aided diagnosisBiology (General)QH301-705.5ENBiology, Vol 10, Iss 1084, p 1084 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	tumor detection convolutional neural network application of tumor detection traditional tumor detection methods computer-aided diagnosis Biology (General) QH301-705.5
spellingShingle	tumor detection convolutional neural network application of tumor detection traditional tumor detection methods computer-aided diagnosis Biology (General) QH301-705.5 Yan Yan Xu-Jing Yao Shui-Hua Wang Yu-Dong Zhang A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network
description	Tumors are new tissues that are harmful to human health. The malignant tumor is one of the main diseases that seriously affect human health and threaten human life. For cancer treatment, early detection of pathological features is essential to reduce cancer mortality effectively. Traditional diagnostic methods include routine laboratory tests of the patient’s secretions, and serum, immune and genetic tests. At present, the commonly used clinical imaging examinations include X-ray, CT, MRI, SPECT scan, etc. With the emergence of new problems of radiation noise reduction, medical image noise reduction technology is more and more investigated by researchers. At the same time, doctors often need to rely on clinical experience and academic background knowledge in the follow-up diagnosis of lesions. However, it is challenging to promote clinical diagnosis technology. Therefore, due to the medical needs, research on medical imaging technology and computer-aided diagnosis appears. The advantages of a convolutional neural network in tumor diagnosis are increasingly obvious. The research on computer-aided diagnosis based on medical images of tumors has become a sharper focus in the industry. Neural networks have been commonly used to research intelligent methods to assist medical image diagnosis and have made significant progress. This paper introduces the traditional methods of computer-aided diagnosis of tumors. It introduces the segmentation and classification of tumor images as well as the diagnosis methods based on CNN to help doctors determine tumors. It provides a reference for developing a CNN computer-aided system based on tumor detection research in the future.
format	article
author	Yan Yan Xu-Jing Yao Shui-Hua Wang Yu-Dong Zhang
author_facet	Yan Yan Xu-Jing Yao Shui-Hua Wang Yu-Dong Zhang
author_sort	Yan Yan
title	A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network
title_short	A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network
title_full	A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network
title_fullStr	A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network
title_full_unstemmed	A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network
title_sort	survey of computer-aided tumor diagnosis based on convolutional neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bb865d24022b4a55a831fadb6e5dd0f2
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A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network

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