Method for Fine Pattern Recognition of Space Targets Using the Entropy Weight Fuzzy-Rough Nearest Neighbor Algorithm

Li, Qingbo; Yuan, Wei; Li, Wenjie

doi:10.1007/s10812-021-01103-9

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…The pattern recognition algorithm cannot accurately identify the glioma margin spectrum due to the complexity and ambiguity of its composition and type. In this article, we use three classification algorithms�support vector machine (SVM), 26 random forest (RF), 27 and weighted entropy fuzzy nearest neighbor (EFRNN) 28 �to identify the margin tissue spectrum and compare it with the results of the MSE abundance estimation, as shown in Table 2. The modeling spectra include 136 normal tissue spectra and 148 glioma spectra.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Mutation Endmember Library Sparse Mixed Abundance Estimation Model for Glioma Margin Determination with Raman Spectroscopy

Li,

Wang

2024

Anal. Chem.

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The glioma margin is a region of brain tissue where glioblastoma tissue transitions to normal tissue with varying levels of cancer cell concentration. This article uses Raman spectroscopy to detect the glioma margin, which is a fuzzy and uncertain substance that cannot be accurately identified by conventional pattern recognition algorithms. This article applies abundance estimation to Raman spectral unmixing of glioma marginal tissues for the accurate and real-time determination of the tumor surgical boundary during an operation. This article introduces a novel method: the mutation endmember library sparse mixed abundance estimation model. This method adds different representative Raman spectra to each endmember library to account for its dynamic properties, thus reducing errors from such variations and fully capturing the diversity within the substance. Moreover, it uses group sparse endmember bundle decomposition, where each substance endmember library consists of multiple Raman spectra. Fractionally mixed norms are used to ensure intergroup and intragroup sparsity, eliminate redundant spectra, and enhance the generalization ability of the abundance estimation. This method was compared with conventional abundance estimation methods. The experimental results of 112 human glioma margin tissues demonstrate that this method outperforms other methods in terms of accuracy, stability, and generalization ability. This article demonstrates the potential of miniature Raman spectroscopy as a new approach to in vivo and noninvasively determining intraoperative margin assessment.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Mutation Endmember Library Sparse Mixed Abundance Estimation Model for Glioma Margin Determination with Raman Spectroscopy

Li,

Wang

2024

Anal. Chem.

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“…When the original spectra to be used for the synthesis were selected, the nearest neighbor number K was replaced by the fuzzy neighbor distance, [25][26][27] as shown in Fig. 4.…”

Section: Algorithm Principlementioning

confidence: 99%

Data augmentation method based on the Gaussian kernel density for glioma diagnosis with Raman spectroscopy

Wang

Zhou³

2023

Anal. Methods

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Location algorithm of transfer stations based on density peak and outlier detection

et al. 2022

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Method for Fine Pattern Recognition of Space Targets Using the Entropy Weight Fuzzy-Rough Nearest Neighbor Algorithm

Cited by 4 publications

References 8 publications

Mutation Endmember Library Sparse Mixed Abundance Estimation Model for Glioma Margin Determination with Raman Spectroscopy

Mutation Endmember Library Sparse Mixed Abundance Estimation Model for Glioma Margin Determination with Raman Spectroscopy

Data augmentation method based on the Gaussian kernel density for glioma diagnosis with Raman spectroscopy

Location algorithm of transfer stations based on density peak and outlier detection

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