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As an emerging technology for artificial intelligence‐aided medical diagnosis, deep learning combined with Raman spectroscopy has great potential. The technology still has some problems in the actual medical diagnosis research process. The differences in spectrometers, experimental conditions, and experimental operations can result in non‐uniform and universally applicable data standards, which in turn lead to low data utilization. At the same time, it is still necessary to retrain the models when building diagnostic models for different diseases, which is time‐consuming and laborious. In this paper, a more complete transfer learning model for multiple types of serum Raman spectra is established for the first time, and a decision fusion strategy is applied to this diagnostic model. The Raman spectral data of serum from hepatitis B patients/control group, serum from abnormal thyroid function patients/control group, and serum from glioma patients/control group were selected as the source domains, and the Raman spectral data of tissue from hepatitis C patients/control group, serum from esophageal cancer patients/control group, and tissue from cervical cancer and cervical inflammation (patients/control) group were selected as the target domains. Three deep neural network models, ResNet, GoogLeNet, and CNN‐LSTM were trained in the source domain data for disease diagnosis, and the trained models were transfer to the target domain. The model is fine‐tuned by freezing different layers and then combined with logistic regression algorithms to construct a decision fusion model, which further improves the model effect. The results show that the proposed method can effectively improve the accuracy of transfer learning models. At the same time, this experiment extends the application of transfer learning in Raman spectroscopy and demonstrates that unrelated and scale‐different Raman datasets are still intrinsically connected, which also lays the foundation for us to build more stable and data‐inclusive spectral transfer learning fusion models in the future. [ABSTRACT FROM AUTHOR]

Screening and diagnosis of early‐stage lung cancer is low worldwide, and when lung cancer progresses to late stage, it will greatly affect the treatment and survival time of patients. Therefore, the development of an affordable and accurate diagnostic technique is critical for lung cancer patients. In this study, we analyzed and verified the changes of substance composition in the serum of early‐stage lung cancer patients and proposed an improved ResNeXt model to achieve accurate classification of serum Raman spectra of lung cancer. The robustness of the model is improved by adding different decibels of Gaussian white noise and spectral offset to augment the data, trying to process the augmented data with various pre‐processing methods, and inputting the pre‐processed data into the improved ResNeXt model, the improved ResNeXt model still shows the optimal performance after sufficient comparison experiments with three other more advanced deep learning models. The experimental results show that the improved ResNeXt model is very suitable for the classification of early‐stage lung cancer serum Raman spectra and may provide a reference for future early screening studies of other cancers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this experiment, we collected 45 samples of cervicitis, 29 samples of low‐grade squamous intraepithelial lesion (LSIL), 44 samples of high‐grade squamous intraepithelial lesion (HSIL), 39 samples of cervical squamous cell carcinoma, and 38 cases of cervical adenocarcinoma. After preprocessing of the Raman spectral data, partial least squares (PLS) was used to reduce the dimensionality, and then extreme gradient boosting (XGBoost) was used for feature selection to obtain the first 30‐dimensional features. The preprocessed Raman spectral data also used a fast Fourier transform (FFT) to obtain amplitude information, and then PLS and XGBoost were used to obtain the first 30‐dimensional features. Finally, K nearest neighbor (KNN), extreme learning machine (ELM), artificial bee colony support vector machine (ABC‐SVM), support vector machine optimized by the cuckoo search algorithm (CS‐SVM), particle swarm optimization coupled with support vector machine (PSO‐SVM), and the convolutional neural network combined with long‐ and short‐term memory (CNN‐LSTM) classification models were established. In the raw Raman spectral features experiments, the classification accuracies of KNN, ELM, ABC‐SVM, CS‐SVM, PSO‐SVM, and CNN‐LTSM were 60.76%, 65.81%, 76.21%, 77.66%, 73.50%, and 69.19%, respectively. In the feature fusion experiments, the classification accuracies were 60.91%, 67.84%, 77.64%, 78.49%, 75.54%, and 70.72%, respectively. The experimental results show that feature fusion can further improve model performance regardless of whether using linear classification models or nonlinear classification models. Therefore, it provides a new strategy for extracting features and screening multiple cervical pathological tissues in the future. [ABSTRACT FROM AUTHOR]