Confocal Raman Sensing Based on a Support Vector Machine for Detecting Lung Adenocarcinoma Cells

Yan, Jing Jou; Shi, Fan; Zhao, Meng; Wang, Zhe; Yang, Yu; Chen, Shengyong

doi:10.1109/jsen.2019.2929701

Cited by 10 publications

(3 citation statements)

References 45 publications

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“…Pulmonary nodules were classified using SVM (Support Vector Machine) [ 10 ]. In this study by extracting nodule morphological features and intensity characteristics to describe the nodule characteristics combined with the log, it boosts a classifier for lung cancer diagnosis [ 11 ]. Literature proposed designing a multilevel 2D cross-convolution.…”

Section: Introductionmentioning

confidence: 99%

Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer

Bhende¹,

Thakare²,

Saravanan³

et al. 2022

BioMed Research International

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At present, early lung cancer screening is mainly based on radiologists’ experience in diagnosing benign and malignant pulmonary nodules by lung CT images. On the other hand, intraoperative rapid freezing pathology needs to analyse the invasive adenocarcinoma nodules with the worst recovery in adenocarcinoma. Moreover, rapid freezing pathology has a low diagnostic accuracy for small-diameter nodules. Because of the above problems, an algorithm for diagnosing invasive adenocarcinoma nodules in ground-glass pulmonary nodules is based on CT images. According to the nodule space information and plane features, sample data of different dimensions are designed, namely, 3D space and 2D plane feature samples. The network structure is designed based on the attention mechanism and residual learning unit; 2D and 3D neural networks are along built. By fusing the feature vectors extracted from networks of different dimensions, the diagnosis results of invasive adenocarcinoma nodules are finally obtained. The algorithm was studied on 1760 ground-glass nodules with 5-20 mm diameter collected from a city chest hospital with surgical and pathological results. There were 340 nodules with invasive adenocarcinoma and 340 with noninvasive adenocarcinoma. A total of 1420 invasive nodule samples were cross-validated on this example dataset. The classification accuracy of the algorithm was 82.7%, the sensitivity was 82.9%, and the specificity was 82.6%.

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Section: Introductionmentioning

confidence: 99%

Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer

Bhende¹,

Thakare²,

Saravanan³

et al. 2022

BioMed Research International

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“…Support vector machine (SVM), a two‐class classification model, is defined as a linear classifier with maximum interval on the feature space, and its learning strategy is interval maximization, which can be eventually translated into the solution of a convex quadratic programming problem. The basic idea is to map the input values into the space in the form of points, and make a line that can distinguish the two types of points and still distinguish them well when new points are introduced, the schematic diagram of the model is shown in Figure S4 [50, 51].…”

Section: Resultsmentioning

confidence: 99%

A rapid and non‐destructive identification for paper cup evidence based on shifted‐excitation Raman difference spectroscopy and SOM clustering

Tian,

Jiang,

Zhang

2023

Journal of Forensic Sciences

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In order to achieve rapid, non‐destructive, efficient, and accurate classification of paper cup samples, we propose a classification model that integrates shifted‐excitation Raman difference spectroscopy (SERDS) with self‐organizing map (SOM) and Bayesian optimization–support vector machine (BO‐SVM). We collected differential Raman data from 52 paper cup samples using SERDS, with an excitation wavelength range of 784–785 nm, a laser power of 440 mW, an integration time of 10 s, and a spectral range spanning from 280 to 2700 cm−1. Subsequently, principal component analysis (PCA) was applied to reduce the dimensionality of the data. The SOM clustering outcomes were utilized as the foundation for constructing the discriminant analysis (FDA) and BO‐SVM classification models. The primary constituent of the paper cup samples was identified as cellulose, while additional fillers such as talc, calcium carbonate, and kaolin were also present. The SOM clustering categorized the samples into seven distinct groups. The FDA model achieved a classification accuracy of 92.3%, and the BO‐SVM model reached a classification accuracy of 96.2%. The SOM clustering effectively discerned samples with different fillers, as evidenced by distinct peak numbers and shapes in the differential Raman spectra, thereby underscoring the practical significance of SOM clustering. In comparison with FDA, BO‐SVM exhibited enhanced classification accuracy and exceptional performance in handling outliers and linearly inseparable data, indicating its superior generalization capabilities.

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“…Pleural effusion is a common complication of lung adenocarcinoma. Detection of tumor cell clusters in pleural effusion is one of the effective means to determine whether there is tumor metastasis from or to the lung [2], [3]. Pleural effusion tumor cell cluster segmentation is a crucial prerequisite for obtaining reliable morphological statistics.…”

Section: Introductionmentioning

confidence: 99%

Fused 3-Stage Image Segmentation for Pleural Effusion Cell Clusters

Zhao

Wang

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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The appearance of tumor cell clusters in pleural effusion is usually a vital sign of cancer metastasis. Segmentation, as an indispensable basis, is of crucial importance for diagnosing, chemical treatment, and prognosis in patients. However, accurate segmentation of unstained cell clusters containing more detailed features than the fluorescent staining images remains to be a challenging problem due to the complex background and the unclear boundary. Therefore, in this paper, we propose a fused 3-stage image segmentation algorithm, namely Coarse segmentation-Mapping-Fine segmentation (CMF) to achieve unstained cell clusters from whole slide images. Firstly, we establish a tumor cell cluster dataset consisting of 107 sets of images, with each set containing one unstained image, one stained image, and one ground-truth image. Then, according to the features of the unstained and stained cell clusters, we propose a three-stage segmentation method: 1) Coarse segmentation on stained images to extract suspicious cell regions-Region of Interest (ROI); 2) Mapping this ROI to the corresponding unstained image to get the ROI of the unstained image (UI-ROI); 3) Fine Segmentation using improved automatic fuzzy clustering framework (AFCF) on the UI-ROI to get precise cell cluster boundaries. Experimental results on 107 sets of images demonstrate that the proposed algorithm can achieve better performance on unstained cell clusters with an F1 score of 90.40%.

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Confocal Raman Sensing Based on a Support Vector Machine for Detecting Lung Adenocarcinoma Cells

Cited by 10 publications

References 45 publications

Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer

Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer

A rapid and non‐destructive identification for paper cup evidence based on shifted‐excitation Raman difference spectroscopy and SOM clustering

Fused 3-Stage Image Segmentation for Pleural Effusion Cell Clusters

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