Raman spectroscopy-based label-free cell identification using wavelet transform and support vector machine

RSC Advances ◽  
2016 ◽  
Vol 6 (55) ◽  
pp. 50027-50033 ◽  
Author(s):  
S. Bakhtiaridoost ◽  
H. Habibiyan ◽  
S. Muhammadnejad ◽  
M. Haddadi ◽  
H. Ghafoorifard ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Raman Spectroscopy ◽  
Wavelet Transform ◽  
Raman Spectra ◽  
Free Cell ◽  
Support Vector ◽  
Label Free ◽  
Cell Identification ◽  
Rare Cells

Wavelet transform and SVM applied to Raman spectra makes a powerful and accurate tool for identification of rare cells such as CTCs.

Label-Free Classification of a Nasopharyngeal Carcinoma Tissue Test at Different Stages Based on Raman Spectroscopy

2017 ◽  
Vol 100 (2) ◽  
pp. 429-433 ◽  
Author(s):  
Mingyu Liu ◽  
Jinyong Lin ◽  
Sufang Qiu ◽  
Weilin Wu ◽  
Gaoqiang Liu ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Nasopharyngeal Carcinoma ◽  
Raman Spectra ◽  
Early Stage ◽  
Support Vector ◽  
Chemical Compositions ◽  
Label Free ◽  
Quick Method ◽  
Clinical Method

Abstract Raman spectroscopy (RS) of nasopharyngeal carcinoma (NPC) tissue provides substantial biomolecular information and various biomedicine features for tissue at different stages of cancer development. This study suggested an automatic and quick method for the classification of Raman spectra at different stages of NPC by multivariate statistical analysis. During RS measurement, Raman spectra were acquired from all NPC tissues in two groups of samples: 30 early-stage NPC patients (stages I and II) and 46 advanced-stage NPC patients (stages III and IV). In addition, a tentative diagnostic algorithm comprising principal components analysis and support vector machine was used to effectively classify multivariate data from the Raman spectra to yield sensitivities (70%; 21 of 30 samples) and specificities (91%; 42 of 46 samples) by the leave-one-out cross-validation method. Meaningful chemical compositions in the classification process were then deduced by analyzing the classified mathematical model. This beneficial work provides a great potential clinical method for the automatic classification of NPC stages and the speculation of the chemical compositions for NPC staging.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

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