Study on the early detection of gastric cancer based on discrete wavelet transformation feature extraction of FT-IR spectra combined with probability neural network

Hu, Tao; Lu, Yuhui; Cheng, Cungui; Xiao-chen, Sun

doi:10.1155/2011/946783

Cited by 13 publications

(5 citation statements)

References 13 publications

(16 reference statements)

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“…In [12] a new texture extraction scheme was presented in CE images for ulcer region discrimination. Local binary pattern based on curvelet is proposed as textural characteristics to differentiate ulcer regions from regular regions, allowing use of curvelet transformation and local binary pattern.…”

Section: Background Reviewmentioning

confidence: 99%

Emprical Study on Gastric Cancer Diagnosis Using Gastroenterology Images

2020

jcr

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According to the worldwide study Gastric Cancer is the third most important type of cancer and it is the second leading cause of death among cancers. As we all know cancer is a disease characterized by abnormal cell growth in the human body. The early diagnosis of this cancer is a critical factor in the healing of patients. About 95% of gastric cancer occurs above the age of 55 and this will supposed to rise from the age of 60. Ulcer is characterized as an area where gastric juices have damaged. Ulcers develop in the stomach, duodenum and small intestine. The cancer polyps and ulcer has same symptoms, so the detection and classification of gastric cancer polyps and gastric ulcer are challenging to the physicians. There are two imaging technologies used for the diagnosis of the stomach cancer and ulcer; chromoendoscopy and narrow-band imaging. In this survey the first section provides an outline about the ailment, whereas in the second section various works related to the diagnosis of the gastric cancer have been discussed. And finally in the conclusion part we propose an early diagnosis method thus reducing the time consumed and classifying the images by using the digital image processing techniques by analyzing the abnormalities of the stomach

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Section: Background Reviewmentioning

confidence: 99%

Emprical Study on Gastric Cancer Diagnosis Using Gastroenterology Images

2020

jcr

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“…Common methods for spectral data compression include increasing the sampling interval, principal component analysis (PCA), Fourier transform, and WT. WT bears the advantages of a high local resolving power, a large compression ratio, and low distortion, and it has been widely used to process chromatograms and infrared and UV‐visible (UV‐vis) spectra . Moreover, the object of LIBS analysis is the characteristic peaks; its frequency is relatively stable and is varied by random system error and the continuous background spectrum.…”

Section: Spectral Data Preprocessingmentioning

confidence: 99%

Chemometrics in laser‐induced breakdown spectroscopy

Zhang

Tang

2018

Journal of Chemometrics

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Laser-induced breakdown spectroscopy (LIBS) is a new type of elemental analytical technology with the advantages of real-time, online, and noncontact as well as enabling the simultaneous analysis of multiple elements. It has become a frontier analytical technique in spectral analysis. However, the issue of how to improve the accuracy of qualitative and quantitative analyses by extracting useful information from a large amount of complex LIBS data remains the main problem for the LIBS technique. Chemometrics is a chemical subdiscipline of multi-interdisciplinary methods; it offers advantages in data processing, signal analysis, and pattern recognition. It can solve some complicated problems that are difficult for traditional chemical methods. In this paper, we reviewed the research progress of chemometrics methods in LIBS for spectral data preprocessing as well as for qualitative and quantitative analyses in the most recent 5 years (2012-2016). KEYWORDSchemometrics, data preprocessing, laser-induced breakdown spectroscopy, qualitative analysis, quantitative analysis | INTRODUCTIONAs one of the most promising analytical tools in the 21st century, laser-induced breakdown spectroscopy (LIBS) is a novel analytical technique for materials and elements; it is based on atomic emission spectroscopy and a laser as an excitation source. 1-4 In LIBS, a low-energy pulse laser (typically tens to hundreds of millijoules per pulse) generated from a laser device is reflected by a plane mirror and focused by a plano-convex lens onto the sample surface; a laser-induced plasma with high temperature is then generated. The light with different frequencies is radiated during the plasma cooling process, and it is collected by an optical fiber coupled to a spectrometer. The qualitative and quantitative analyses by the LIBS technique can be performed using the wavelength and integrated intensity of the spectral line. A schematic diagram of the LIBS instrument is presented in Figure 1. It consists of Nd:YAG laser, optical part, X-Y-Z manual micrometric stage, optical fiber, spectrometer, Charge coupled device (CCD) camera, and computer.The LIBS technology has benefited from the invention of the ruby crystal laser 5 and was first reported by Brech and Cross. 6 It has become a research focus in the spectral field with the development of laser and optical detection techniques. Compared with conventional analytical tools, LIBS demonstrates some substantial advantages with regards to being rapid, real-time, on-site, and nondestructive while allowing remote detection without complicated sample preparation procedures as well as simultaneous multicomponent analysis. 7-9 Because of these advantages, LIBS technology has been widely used in process analysis and in the control of industrial manufacturing, food supervision, environmental monitoring, historical relic identification, and space exploration. [10][11][12][13] The National Aeronautics and

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“…These biomolecules are closely tied to the carcinogenesis of cells and tissues. Thus, FTIR spectroscopy has been used to investigate various tumors, including breast cancer [3,4] , gastric cancer [5] and leukemia [6] . Related research results show that FTIR spectroscopy can be utilized to detect cancer at the early stage, differentiate cancerous tissue from normal tissue, and even can be used to define surgical boundaries of cancer resection [7] .…”

Section: Introductionmentioning

confidence: 99%

Detection of cholangiocarcinoma with Fourier transform infrared spectroscopy and radial basis function neural network classification

Cui

Ling

et al. 2016

Chem. Res. Chin. Univ.

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The aim of this study was to explore the possibility of applying Fourier transform infrared(FTIR) spectroscopy as a medical diagnostic tool based on a neural network classifier for detecting and classifying cholangiocarcinoma. A total of 51 cases of bile duct tissues were obtained and later characterized by FTIR spectroscopy prior to pathological diagnosis. The criteria for classification included 30 parameters for each FTIR spectra, including peak position(P), intensity(I) and full width at half-maximum(FWHM), were measured, calculated and subsequently compared against the normal and cancer groups. The FTIR spectra were classified by the radial basis function(RBF) network model. For establishing the RBF, 23 cases were used to train the RBF classifier, and 28 cases were applied to validate the model. Using the RFB model, nine parameters were observed to be pronouncedly different between cancerous and normal tissue, including I and P 1040 . In the RBF training classification, the accuracy, sensitivity, and specificity of diagnosis were 82.6%, 80.0%, and 84.6%, respectively. While validating the classification, the accuracy, sensitivity, and specificity of diagnosis were 78.6%, 75.0%, and 81.2%, respectively. The results suggest that FTIR spectroscopy combined with neural network classifier could be applied as a medical diagnostic tool in cholangiocarcinoma diagnosis.

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Study on the early detection of gastric cancer based on discrete wavelet transformation feature extraction of FT-IR spectra combined with probability neural network

Cited by 13 publications

References 13 publications

Emprical Study on Gastric Cancer Diagnosis Using Gastroenterology Images

Emprical Study on Gastric Cancer Diagnosis Using Gastroenterology Images

Chemometrics in laser‐induced breakdown spectroscopy

Detection of cholangiocarcinoma with Fourier transform infrared spectroscopy and radial basis function neural network classification

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