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.
A photonic artificial intelligence chip is based on an optical neural network (ONN), low power consumption, low delay, and strong antiinterference ability. The all-optical diffractive deep neural network has recently demonstrated its inference capabilities on the image classification task. However, the size of the physical model does not have miniaturization and integration, and the optical nonlinearity is not incorporated into the diffraction neural network. By introducing the nonlinear characteristics of the network, complex tasks can be completed with high accuracy. In this study, a nonlinear all-optical diffraction deep neural network (N-D2NN) model based on 10.6 μm wavelength is constructed by combining the ONN and complex-valued neural networks with the nonlinear activation function introduced into the structure. To be specific, the improved activation function of the rectified linear unit (ReLU), i.e., Leaky-ReLU, parametric ReLU (PReLU), and randomized ReLU (RReLU), is selected as the activation function of the N-D2NN model. Through numerical simulation, it is proved that the N-D2NN model based on 10.6 μm wavelength has excellent representation ability, which enables them to perform classification learning tasks of the MNIST handwritten digital dataset and Fashion-MNIST dataset well, respectively. The results show that the N-D2NN model with the RReLU activation function has the highest classification accuracy of 97.86% and 89.28%, respectively. These results provide a theoretical basis for the preparation of miniaturized and integrated N-D2NN model photonic artificial intelligence chips.
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