Classification of Rat FTIR Colon Cancer Data Using Wavelets and BPNN

Cheng, Cungui; Xiong, Wei; Tian, Yuan

doi:10.1002/cjoc.200990154

Cited by 6 publications

(7 citation statements)

References 7 publications

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“…The results of the studies carried out on the separation of colon cancer patients and healthy subjects using tissue samples are as follows: Cheng et al [50] obtained 100%, 97.5%, 95%, and 100% accuracy, respectively, in the classification of normal, dysplasia, early carcinoma, and advanced cancer tissues using SVM. Then Cheng et al [17] classified the normal, dysplasic, early carcinoma, and advanced cancer tissues and obtained 100%, 94%, 97.5%, and 100% accuracy, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…One of the methods employed to distinguish between the existence of cancer and healthy subjects is FTIR spectroscopy. Many studies have been conducted on the distinction of cancerous and normal tissue by FTIR in prostate cancer [5], cervical cancer [6][7][8], pancreatic cancer [9], gastric tissues * Correspondence: storaman@firat.edu.tr [10][11][12], esophagus cancer [13], breast cancer [14,15], and colon cancer [16][17][18][19]. In addition, various studies have recently been carried out for the separation of patients and healthy subjects using blood samples and FTIR signals in leukemia [20,21], Parkinson disease [22], gastric cancer [23], and other types of cancer such as breast, gastrointestinal, lung [3,24], and colon cancer [4,25].…”

Section: Introductionmentioning

confidence: 99%

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Classification of the likelihood of colon cancer with machine learning techniquesusing FTIR signals obtained from plasma

Toraman¹,

Gırgın²,

Üstündağ³

et al. 2019

Turk J Elec Eng & Comp Sci

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Colon cancer is one of the major causes of human mortality worldwide and the same can be said for Turkey. Various methods are used for the determination of cancer. One of these methods is Fourier transform infrared (FTIR) spectroscopy, which has the ability to reveal biochemical changes. The most common features used to distinguish patients with cancer and healthy subjects are peak densities, peak height ratios, and peak area ratios. The greatest challenge of studies conducted to distinguish cancer patients from healthy subjects using FTIR signals is that the signals of cancer patients and healthy subjects are similar. In the current study, a method in which the area and height ratios of the FTIR signal, as well as various statistical features, are proposed in order to overcome this difficulty. Blood samples (plasma) were collected from 30 colon cancer patients and 40 healthy subjects, and FTIR measurements were performed. A total of 16 features were obtained, including five height ratios, five area ratios, and six statistical features, from each FTIR signal. The 16 features were classified with a multilayer perceptron neural network and support vector machines using cross-validation and their performances were then compared. The current study demonstrated that different features obtained from plasma FTIR spectra can be used together in order to distinguish colon cancer patients from healthy individuals.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of the likelihood of colon cancer with machine learning techniquesusing FTIR signals obtained from plasma

Toraman¹,

Gırgın²,

Üstündağ³

et al. 2019

Turk J Elec Eng & Comp Sci

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“…However, single FT-IR spectra analysis can not be quickly and accurately applied in the analysis and identification of complex systems like traditional Chinese medicines. Chemometric methods combined with FT-IR spectroscopy can make up the defects in single FT-IR spectra analysis [2,4,[6][7][8]15]. So we choose FT-IR spectra with several chemometric methods to discriminate different plants of the same family.…”

Section: Introductionmentioning

confidence: 99%

Classification of Five Kinds of Moss Plants with the Use of Fourier Transform Infrared Spectroscopy and Chemometrics

Jin

Cheng

2011

Spectroscopy

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Fourier transform infrared spectroscopy (FT-IR) with Horizontal Attenuated Total Reflectance (HATR) techniques is used to obtain the FT-IR spectra of five kinds of mosses, such asPtychomitrium dentatum(Mitt.) Jaeg.,Ptychomitrium polyphylloides(C. Muell.) Par.,Ptychomitrium sinense(Mitt.) Jaeg.,Macromitrium syntrichophyllumTher. Etp. Vard., andMacromitrium ferrieiCard. Sz Ther. Based on the comparison of the above mosses in the FT-IR spectra, the region ranging from 4000 to 650 cm−1was selected as the characteristic spectra for analysis. Principal component analysis (PCA) and cluster analysis are considered to identify the five moss species. Because they belong to the homogeneous plants, and have similar chemical components and close FT-IR spectroscopy, PCA and cluster analysis can only give a rough result of classification among the five moss species, Fourier self-deconvolution (FSD) and discrete wavelet transform (DWT) methods are used to enhance the differences between them. We use these methods for further study. Results show that it is an excellent method to use FT-IR spectroscopy combined with FSD and DWT to classify the different species in the same family. FT-IR spectroscopy combined with chemometrics, such as FSD and DWT, can be used as an effective tool in systematic research of bryophytes.

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“…So we can use the FTIR to classify different plants. 2,3 But in many practical problems, FTIR analysis can not be quickly and accurately used in which we want to make judgments. Chemometrics is the application of mathematical or statistical methods to chemical data.…”

Section: Introductionmentioning

confidence: 99%

Identification between Fimbristylis miliacea (L.) Vahl and Fimbristhlis stauntonii Debeaux et Franch. by CWT‐FTIR‐RBFNN

Qing-hong

Zheng

et al. 2011

Chin. J. Chem.

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Horizontal attenuation total reflection-Fourier transform infrared spectroscopy (HATR-FTIR) is used to measure the FTIR of Fimbristylis miliacea (L.) Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed. In order to extrude the difference between Fimbristylis miliacea (L.) Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed, continuous wavelet transform (CWT) is used to decompose the FTIR of Fimbristylis miliacea (L.) Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed. Three main scales are selected as the feature extracting space in the CWT domain. According to the distribution of FTIR of Fimbristylis miliacea (L.) Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed, three feature regions are determined at every spectra band at selected three scales in the CWT domain. Thus nine feature parameters form the feature vector. The feature vector is input to the radial basis function neural network (RBFNN) to train so as to accurately classify the Fimbristylis miliacea (L.) Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed. 110 couples of FTIR are used to train and test the proposed method, where 60 couples are used as training samples and 50 couples are used as testing samples. Experimental results show that the accurate recognition rate between Fimbristylis miliacea (L.) Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed is respectively 96% and 98% by using the proposed method. Keywords IR, continuous wavelet transform, radius basis function neural network, Fimbristylis miliacea (L.) Vahl, Fimbristhlis stauntonii debeaux et Franch. Identification between Fimbristylis miliacea (L.) Vahl and Fimbristhlis stauntonii Debeaux et Franch.

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Classification of Rat FTIR Colon Cancer Data Using Wavelets and BPNN

Cited by 6 publications

References 7 publications

Classification of the likelihood of colon cancer with machine learning techniquesusing FTIR signals obtained from plasma

Classification of the likelihood of colon cancer with machine learning techniquesusing FTIR signals obtained from plasma

Classification of Five Kinds of Moss Plants with the Use of Fourier Transform Infrared Spectroscopy and Chemometrics

Identification between Fimbristylis miliacea (L.) Vahl and Fimbristhlis stauntonii Debeaux et Franch. by CWT‐FTIR‐RBFNN

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