A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images.

Min Xu, Pengjiang Qian, Jiamin Zheng, Hongwei Ge, Raymond F Muzic

Abstract read

In one paragraph

Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Min XuSchool of Internet of Things Technology, Wuxi Institute of Technology, Wuxi 214121, China.ORCID https://orcid.org/0000-0002-1679-1236

Pengjiang QianJiangsu Key Lab of Media Design and Software Technology, Jiangnan University, Wuxi 214122, China.ORCID https://orcid.org/0000-0002-5596-3694

Jiamin ZhengSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.

Hongwei GeSchool of Internet of Things, Jiangnan University, Wuxi 214122, China.ORCID https://orcid.org/0000-0002-1413-0303

Raymond F MuzicDepartment of Radiology and Case Center for Imaging Research, University Hospitals, Case Western Reserve University, Cleveland, OH 44106, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific target areas (organs) based on MR images is one of the key issues in computer-aided diagnosis of medical images. Artificial neural network technology has made significant progress in image processing based on the multimodal MR attributes of each pixel in MR images. However, with the generation of large-scale data, there are few studies on the rapid processing of large-scale MRI data. To address this deficiency, we present a fast radial basis function artificial neural network (Fast-RBF) algorithm. The importance of our efforts is as follows: (1) The proposed algorithm achieves fast processing of large-scale image data by introducing the

Indexed as

AlgorithmsNeural Networks, ComputerAbdomenComputational BiologyHumansImage Interpretation, Computer-AssistedKidneyLiverMagnetic Resonance ImagingOrgan SpecificitySupport Vector Machine

Identifiers

PMID32454883

DOI10.1155/2020/4519483

PMCPMC7222610

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.