Neural network data analysis for laser-induced thermal acoustics

Schlamp, Stefan; Hornung, H. G.; Cummings, Eric B.

doi:10.1088/0957-0233/11/6/323

Cited by 12 publications

(12 citation statements)

References 24 publications

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“…The construction of ANN is mainly considered the neural network layers, the number of nodes in each layer and the selection of training algorithms. 42,43 Figure 3 shows the established ANN's architecture with an input layer, a hidden layer, and an output layer which are interconnected with different weights. The network was trained by the following parameters: learning rate, training epochs, convergence error, and minimum performance gradient.…”

Section: Ann Establishmentmentioning

confidence: 99%

Identification of oral precancerous and cancerous tissue by swept source optical coherence tomography

et al. 2021

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Background and Objectives: Distinguishing cancer from precancerous lesions is critical and challenging in oral medicine. As a noninvasive method, optical coherence tomography (OCT) has the advantages of real-time, in vivo, and large-depth imaging. Texture information hidden in OCT images can provide an important auxiliary effect for improving diagnostic accuracy. The aim of this study is to explore a reliable and accurate OCT-based method for the screening and diagnosis of human oral diseases, especially oral cancer. Materials and Methods: Fresh ex vivo oral tissues including normal mucosa, leukoplakia with epithelial hyperplasia (LEH), and oral squamous cell carcinoma (OSCC) were imaged intraoperatively by a homemade OCT system, and 58 texture features were extracted to create computational models of these tissues. A principal component analysis algorithm was employed to optimize the combination of texture feature vectors. The identification based on artificial neural network (ANN) was proposed and the sensitivity/specificity was calculated statistically to evaluate the classification performance. Results: A total of 71 sites of three types of oral tissues were measured, and 5176 OCT images of three types of oral tissues were used in this study. The superior classification result based on ANN was obtained with an average accuracy of 98.17%. The sensitivity and specificity of normal mucosa, LEH, and OSCC are 98.17% / 98.38%, 93.81% / 98.54%, and 98.11% / 99.04%, respectively. Conclusion: It is demonstrated from the high accuracies, sensitivities, and specificities that texture-based analysis can be used to identify oral precancerous and cancerous tissue in OCT images, and it has the potential to help surgeons in diseases screening and diagnosis effectively.

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Section: Ann Establishmentmentioning

confidence: 99%

Identification of oral precancerous and cancerous tissue by swept source optical coherence tomography

et al. 2021

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“…The number of hidden layer(s) and that of neurons in each hidden layer as well as the tuning parameters of BP algorithm are usually adjusted based on empirical trial and error performance. However, it is not difficult to adjust these parameters in order to obtain the satisfactory results [18]. Since the training and testing phases of ANN are fully independent, ANN can provide output very fast once it is trained.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…On the other hand, artificial neural network (ANN) has been widely employed for different applications of estimation in science and engineering to achieve complex inputoutput relationship as well as nonlinear mapping ability in recent years [15][16][17][18][19][20][21][22][23]. It outperforms other conventional nonlinear methods since it does not require much prior inputoutput relationships on the nature of nonlinearity existing between the input and output patterns [21,22].…”

Section: Introductionmentioning

confidence: 99%

Signal processing using artificial neural network for BOTDA sensor system

et al. 2016

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We experimentally demonstrate the use of artificial neural network (ANN) to process sensing signals obtained from Brillouin optical time domain analyzer (BOTDA). The distributed temperature information is extracted directly from the local Brillouin gain spectra (BGSs) along the fiber under test without the process of determination of Brillouin frequency shift (BFS) and hence conversion from BFS to temperature. Unlike our previous work for short sensing distance where ANN is trained by measured BGSs, here we employ ideal BGSs with different linewidths to train the ANN in order to take the linewidth variation due to different conditions from the training and testing phases into account, making it feasible for long distance sensing. Moreover, the performance of ANN is compared with other two techniques, Lorentzian curve fitting and cross-correlation method, and our results show that ANN has higher accuracy and larger tolerance to measurement error, especially at large frequency scanning step. We also show that the temperature extraction from BOTDA measurements employing ANN is significantly faster than the other two approaches. Hence ANN can be an excellent alternative tool to process BGSs measured by BOTDA and obtain temperature distribution along the fiber, especially when large frequency scanning step is adopted to significantly reduce the measurement time but without sacrifice of sensing accuracy.

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“…The evolution of laser induced gratings arising from density perturbation in the gas is described by a linearised set of hydrodynamic equations by Paul et al [4] and by Cummings [5]. Several methods have been developed to solve these equations including approximations in the time domain that do not, however, distinguish details of the quenching process [14]. The analysis by Hemmerling and Kozlov for the particular case of pure molecular oxygen illustrates the potential of the LIGS technique for relaxation studies in gases but the approach is not easily generalised [10,11,15].…”

Section: Ligs Modelmentioning

confidence: 99%