Tongue squamous cell carcinoma discrimination with Raman spectroscopy and convolutional neural networks

Hu, Yan; Yu, Mingxin; Xia, Jiabin; Zhu, Lianqing; Zhang, Tao; Zhu, Zhihui

doi:10.1016/j.vibspec.2019.102938

Cited by 42 publications

(33 citation statements)

References 27 publications

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“…In Table 6, new CNN refers to only retaining the full spectrum channel in the ensemble CNN, and past CNN refers to the CNN model in the previous paper. [45] The results of the CNN model show that in the case of normal tissue, 219 of the 224 spectra are correctly classified. In the case of proper tumor tissue, 170 of the 176 spectra were correctly classified.…”

Section: Spectral Data Analysismentioning

confidence: 99%

“…In order to verify whether the proposed method is superior, a simple CNN model is selected and compared with our method under the same number of layers and parameters. In addition, the CNN model used in the previous article is also used, which has only five layers of neural networks to compare the model capabilities in the full spectrum [45]. Figure 9 shows the accuracy and loss curves of this model for this data set.…”

Section: Spectral Data Analysismentioning

confidence: 99%

“…Therefore, the accuracy of tumor tissue recognition is improved. In our previous article [45,46], the data we collected was only from 12 people in total, with a total of 24 tissues, and the spectral range was only 600-1800 cm -1 . In this experiment, 22 patients were obtained, a total of 44 organizations, a significant increase compared to the previous data size.…”

Section: Introductionmentioning

confidence: 99%

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Diverse Region-Based CNN for Tongue Squamous Cell Carcinoma Classification With Raman Spectroscopy

Xia

et al. 2020

IEEE Access

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Border discrimination is very important in the treatment of tongue squamous cell carcinoma (TSCC). This study proposes an ensemble convolutional neural network (CNN) framework based on fiber optic Raman spectroscopy and deep learning techniques to distinguish between TSCC and non-tumor tissue frameworks. First, the data used in the experiments was collected by a fiber optic Raman system. A total of 44 tissues of 22 patients were collected for Raman spectroscopy, with TSCC and adjacent normal tissues each accounting for half. The spectral data range used in the model from a full spectrum of 600-4000 cm-1. Then, the ensemble CNN model was used in the experiment. By using two convolution kernels, the model is able to extract nonlinear feature representations from different spectral regions. It has two advantages, on the one hand, it reduces the generation of noise, on the other hand, it obtains a stronger distinguishing ability. Finally, a feature vector is formed by the fusion layer, and is sent to the fully connected layer for the TSCC classification task. The results showed that the sensitivity and specificity of the model were 99.2% and 99.2%, respectively. In addition, comparison with existing methods shows that our method achieves the highest accuracy of TSCC classification. By comparing the different channels, the results show that the spectral range of 1380-2250cm-1 data has the greatest impact on the results. Therefore, Raman spectroscopy combined with the ensemble CNN model has great potential and can provide a useful technique for intraoperative evaluation of the margins of oral tongue squamous cell carcinoma.

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Section: Spectral Data Analysismentioning

confidence: 99%

Section: Spectral Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diverse Region-Based CNN for Tongue Squamous Cell Carcinoma Classification With Raman Spectroscopy

Xia

et al. 2020

IEEE Access

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“…On the basis of CNN, residual network (Resnet) avoid the problem of gradient explosion or gradient disappearance which caused by deep structures by adding residual blocks 18 . To our knowledge, CNNs have been used in the analysis of vibrational spectral data 19–21 . However, it is rare to understand the internal structure of the sample by increasing the depth of the model to explore the deep‐level characteristic information of the spectrum.…”

Section: Introductionmentioning

confidence: 99%

“…18 To our knowledge, CNNs have been used in the analysis of vibrational spectral data. [19][20][21] However, it is rare to understand the internal structure of the sample by increasing the depth of the model to explore the deep-level characteristic information of the spectrum.…”

Section: Introductionmentioning

confidence: 99%

End‐to‐end analysis modeling of vibrational spectroscopy based on deep learning approach

Xin¹,

Tian³

et al. 2020

Journal of Chemometrics

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The characteristics of the spectral data are essential for the qualitative analysis of substances. Traditional classification models often need to preprocess the data. However, misuse of preprocessing may change the characteristic information carried by the original data which result in poor model performance. This paper proposes an end-to-end deep learning method that combines residual modules to learn features from raw data to improve model performance, which called ResidualSpectra. ResidualSpectra model is compared with three convolutional neural network (CNN) models on the original data. The 15 preprocessing approaches are used to evaluate the preprocessing impact by testing five open-access mid-infrared, near-infrared, and Raman spectra datasets (fruits, meats, olive_oils, Tablets_Nir, Tablets_Raman). In most cases, the ResidualSpectra model performs better than the other three CNN models on five datasets and obtains better results in original data than in preprocessed data. The model is compared with linear discriminant analysis (LDA), nonlinear artificial neural network (ANN), support vector machines (SVM) for original and preprocessed data. The results show that the ResidualSpectra method provides improved results over traditional classification methods in most scenarios.

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Refinement of spectra using a deep neural network: Fully automated removal of noise and background

Gebrekidan

Knipfer

Braeuer

2021

J Raman Spectroscopy

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We report the potential of U‐Net deep neural network for the efficient removal of noise and background from raw Raman spectra. The U‐Net method was first trained on simulated spectra and then tested with experimental spectra. The quality of the test results was quantified via different signal‐to‐noise ratios and the structural similarity index metric. The U‐Net recovered Raman spectra feature a high structural similarity index, even for raw spectra that were dominated by background. The U‐Net model does not rely on any human intervention.

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Tongue squamous cell carcinoma discrimination with Raman spectroscopy and convolutional neural networks

Cited by 42 publications

References 27 publications

Diverse Region-Based CNN for Tongue Squamous Cell Carcinoma Classification With Raman Spectroscopy

Diverse Region-Based CNN for Tongue Squamous Cell Carcinoma Classification With Raman Spectroscopy

End‐to‐end analysis modeling of vibrational spectroscopy based on deep learning approach

Refinement of spectra using a deep neural network: Fully automated removal of noise and background

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