Label‐free serum detection based on Raman spectroscopy for the diagnosis and classification of glioma

Medical diagnosis technology based on convolutional neural networks (CNNs) has achieved good performance. In this study, we collected serum samples from 38 glioma patients and 45 healthy controls and used partial least squares (PLS) analysis to reduce the dimension of the data. Different levels of noise were added to the reduced data onto data augmentation, and the AlexNet, ResNet, and GoogLeNet fine‐tuning models were applied for classification. To evaluate the performance of the models, we used five‐fold cross‐validation. The accuracy rates of the AlexNet, ResNet, and GoogLeNet fine‐tuning models were 98.50%, 98.24%, and 99.50%, respectively. The model with the best classification effect was GoogLeNet. The specificity and sensitivity of this model were 98.98% and 98.48%, respectively. In addition, the area under the receiver operating characteristic (ROC) curve (AUC) of the established diagnostic model was 0.9949. The results showed that the combination of serum Raman spectroscopy and the PLS‐Gaussian‐GoogLeNet model achieved a good diagnostic effect for glioma. This method has high clinical application value and is worthy of further popularization.

Section: Resultsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Application of Raman spectroscopy technology based on deep learning algorithm in the rapid diagnosis of glioma

Tian

Chen

et al. 2022

“…For a study of serum Raman spectroscopy for the diagnosis of glioma, Zhang et al used principal component analysis (PCA)‐LDA to classify patients with glioma (high‐grade glioma, low‐grade glioma) and control subjects into three groups, and the classification accuracy of the three groups was 96.27%, 97.95%, and 98.52%. [ 16 ] However, because of the lack of cross‐validation in this experiment, the results are not necessarily reliable. Moreover, PCA retained too many principal components after dimensionality reduction, which affected the convergence rate of the model to some extent.…”

Section: Introductionmentioning

confidence: 96%

Rapid diagnosis of lung cancer and glioma based on serum Raman spectroscopy combined with deep learning

Chen

et al. 2021

Lung cancer and glioma are common malignancies worldwide and pose a serious threat to human health. There may be a certain correlation between lung cancer patients and glioma patients in serum composition, but to date, no study on the classification and correlation of lung cancer and glioma is available. In this paper, the differences and relationships between lung cancer and glioma were analyzed from serum Raman spectra. The existing detection methods of lung cancer and glioma are time consuming and expensive, so we propose a method based on patient serum Raman spectra combined with deep learning, which can screen lung cancer and glioma accurately with speed and low cost. In this study, features were extracted from the original spectral data of patients with lung cancer, glioma, and control subjects. By adding different decibels of white Gaussian noise to the training set for data enhancement, the enhanced training set data were imported into a multilayer perceptron (MLP), recursive neural network (RNN), convolutional neural network (CNN), and AlexNet using fivefold cross‐validation to build the diagnostic model. The results show that PLS‐AlexNet is the best model. The accuracy of this model in the binary classification experiment of lung cancer and control subjects, lung cancer and glioma, and glioma and control subjects were 99%, 95.2%, and 100%, respectively, and the experimental accuracy of the AlexNet triclassification algorithm is also above 85%. This method has great potential in clinical diagnosis of diseases.

“…[ 11–14 ] Raman spectroscopy has unique advantages in studying cellulose [ 15 ] and lignin [ 16 ] of paper and papermaking raw materials. Recently, the combination of spectroscopy and machine learning [ 17–19 ] provided more in‐depth insights for material classification and recognition, because machine learning is a powerful method that can find hidden rules from a large amount of data. In this study, 18 types of handmade paper samples were measured using a Raman spectrometer and classified and identified by various machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Analysis of handmade paper by Raman spectroscopy combined with machine learning

Yan

Cheng

Luo

et al. 2021

Handmade paper is a major carrier and restoration material of traditional Chinese ancient books, calligraphies, and paintings. In this study, we carried out a Raman spectroscopy analysis of 18 types of handmade paper samples. The main components of the handmade paper were cellulose and lignin, according to the wavenumber and Raman vibration assignment. We divided its Raman spectrum into eight subbands. Five machine learning models were employed: principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), k‐nearest neighbors (KNN), and random forest (RF). The Raman spectral data were normalized, and the fluorescence envelope was subtracted using the airPLS algorithm to obtain four types of data, raw, normalized, defluorescence, and fluorescence data. An RF variable importance analysis of data processing showed that data normalization eliminated the intensity differences of fluorescence signals caused by lignin, which contained important information of raw materials and papermaking technology, let alone the data defluorescence. The data processing also reduced the importance of the average variables in almost all spectral bands. Nevertheless, the data processing is worthwhile because it significantly improves the accuracy of machine learning, and the information loss does not affect the prediction. Using the machine learning models of PCA, PLS, and SVM combined with linear regression (LR), KNN, and RF, the classification and prediction of handmade paper samples were realized. For almost all processed data, including the fluorescence data, PCA‐LR had the highest classification and prediction accuracy (R2 = 1) in almost all spectral bands. PLS‐LR and SVM‐LR had the second‐highest accuracies (R2 = 0.4–0.9), whereas KNN and RF had the lowest accuracies (R2 = 0.1–0.4) for full band spectral data. Our results suggest that the abundant information contained in Raman spectroscopy combined with powerful machine learning models could inspire further studies on handmade paper and related cultural relics.