NDLI: Optimisation analysis of pulmonary nodule diagnostic test based on deep belief nets

Content Provider	IET Digital Library
Author	Yang, Wei Xia, Wenhua Xie, Yuanliang Mao, Shilong Li, Rong
Abstract	At present, the rate of missed diagnosis of lung cancer is high. The reason is that the pulmonary nodule phenomenon cannot be effectively monitored due to various interference factors in the actual detection process. In order to improve the detection accuracy, this study combined with the actual situation to analyse the diversity of nodular shape and constructed a deep belief network-based diagnosis model for pulmonary nodules. At the same time, in order to improve the detection effect, this study sets the model to have multi-layer non-linear structure and analyses the previous clinical data to improve the model learning rate and training effect. In addition, in order to verify the performance of the model, the diagnostic effect of the model is studied by comparative experiments. The research shows that the model proposed in this study is higher than the traditional algorithm in detection accuracy, which can provide theoretical reference for subsequent related research.
Starting Page	1227
Ending Page	1232
Page Count	6
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 7, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1022
Journal	IET Image Processing
Publisher Date	2019-10-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Actual Detection Process Belief Network Biology And Medical Computing Cancer Computer Vision And Image Processing Technique Deep Belief Nets Deep Belief Network-based Diagnosis Model Detection Accuracy Detection Effect Diagnostic Effect Fault Diagnosis Interference Factors Knowledge Engineering Technique Learning in AI Lung Lung Cancer Medical Image Processing Missed Diagnosis Model Learning Rate Multilayer Nonlinear Structure Nodular Shape Optical, Image And Video Signal Processing Optimisation Analysis Patient Diagnosis Patient Diagnostic Method And Instrumentation Previous Clinical Data Pulmonary Nodule Diagnostic Test Pulmonary Nodule Phenomenon Pulmonary Nodules Statistics Training Effect
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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