Deep Learning For Computational Spectrometer Design And Vein Thrombus Diagnosis

Deep learning,as a machine learning technique based on artificial neural networks,has been widely applied in various fields including medicine,computer vision,natural language processing,audio signal processing,financial risk management,intelligent transportation,and robot control,achieving significant results.With the continuous development and improvement of technology,deep learning will play its powerful pattern recognition and adaptive capabilities in more fields,providing more efficient,accurate,and reliable solutions to practical problems.This article mainly introduces the applications and research of deep learning in the design of computational spectrometer filter array and contrast enhanced ultrasound.As one of the core components of a computational spectrometer,the design of the filter array is crucial for the performance of the spectrometer.Traditional filter array designs require a lot of time for simulation and iterative optimization of structural parameters,leading to high costs.Introducing advanced AI technology to build a filter array design method can significantly reduce costs and improve performance.This study constructs a filter array design method for computational spectrometer based on deep learning.First,the finite difference time-domain method is used to simulate photonic crystal structures to obtain transmission spectra.Then,the correlation coefficient formula is used to calculate the correlation coefficients between transmission spectra,and a dataset of structure parameters and correlation coefficients is constructed.A tandem network architecture is used for inverse design of arbitrary correlation coefficient photonic crystal structures,and a screening algorithm is used to form the filter array of the photonic crystal structure in the computational spectrometer.The compressive sensing algorithm is also introduced to efficiently recover the spectral information of the original signal from compressed sampling data for spectral reconstruction.The effect of spectral reconstruction is related to the size and average correlation coefficient of the filter array,and the larger the size,the better the reconstruction effect;meanwhile,for smaller arrays,the lower the correlation coefficient,the higher the limit accuracy of spectral reconstruction.Contrast enhanced ultrasound is a non-invasive,low-cost,real-time medical imaging technology with advantages such as observing physiological parameter changes,portability,and noninvasiveness.However,its deep tissue imaging effect is relatively poor,and there is room for improvement in resolution and imaging ability for small structures,and it requires professional training and experience for operators.Using deep learning techniques for assisted diagnosis has become one of the current research hotspots.It can extract useful feature information by learning and analyzing large amounts of image data to realize automatic analysis,recognition,and judgment of images.The introduction of deep learning technology can effectively reduce the threshold of ultrasound imaging.By using an ultrasound imaging dataset of vein,the convolutional neural network Res Net50 is applied to identify thrombosis of acute and chronic types,and the Grad-CAM algorithm is introduced to transform the gradient features in the CNN into a thrombus heat map.The Faster-R-CNN object detection model is used to automatically recognize and track thrombus in complex tissues.These methods can improve the accuracy and efficiency of thrombus identification,and are expected to achieve more accurate and efficient ultrasound imaging analysis and diagnosis in medicine.This article introduces the applications and research of deep learning in the design of computational spectrometer filter arrays and ultrasound imaging.For traditional filter array design methods that require a lot of time for simulation and iterative optimization,this study constructs a filter array design method for computational spectrometers based on deep learning,which can reduce costs and improve performance.The application of deep learning technology in ultrasound imaging can extract useful feature information by learning and analyzing large amounts of image data and realizing automatic analysis,recognition,and judgment of images,thereby improving the accuracy and efficiency of thrombus identification.These applications demonstrate the powerful pattern recognition and adaptive capabilities of deep learning in practical problems,providing more efficient,accurate,and reliable solutions to practical problems,and will continue to be widely used in more fields in the future.