Study on the Detection of Pulmonary Nodules in CT Images Based on Deep Learning

With the development of medical imaging technology and the introduction of computed tomography (CT), early screening for lung cancer is becoming more and more possible. In this paper, we introduce the method of wavelet dynamic analysis to extract and repair the lung parenchyma, so as to exclude the noise interference outside the lung parenchyma. The algorithm can help us to locate the lung nodules with higher accuracy. Then, the convolution neural network (CNN) optimized by genetic algorithm and the traditional CNN are used to extract the features of CT image of pulmonary nodules. The corresponding features of different images are automatically distinguished. By comparing the accuracy of the two algorithms, it is proved that the CNN optimized by genetic algorithm has higher accuracy. Finally, the CNN optimized by genetic algorithm is used to detect and classify the existing pulmonary nodule images, which provides guidance for CT image detection technology of pulmonary nodule.

[1]  Yudong Zhang,et al.  Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling , 2017, Journal of Medical Systems.

[2]  Alin Achim,et al.  Novel Bayesian multiscale method for speckle removal in medical ultrasound images , 2001, IEEE Transactions on Medical Imaging.

[3]  Nima Tajbakhsh,et al.  Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs , 2017, Pattern Recognit..

[4]  Hao Chen,et al.  Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection , 2017, IEEE Transactions on Biomedical Engineering.

[5]  Temesguen Messay,et al.  A new computationally efficient CAD system for pulmonary nodule detection in CT imagery , 2010, Medical Image Anal..

[6]  Lubomir M. Hadjiiski,et al.  Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours. , 2006, Medical physics.

[7]  Spyridon Kontaxis,et al.  Assessment of Quadratic Nonlinear Cardiorespiratory Couplings During Tilt-Table Test by Means of Real Wavelet Biphase , 2019, IEEE Transactions on Biomedical Engineering.

[8]  Mingzhe Liu,et al.  Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm , 2015, Neurocomputing.

[9]  Application of continuous wavelet transform to the analysis of structural variations in complex networks , 2015 .

[10]  Qiuming Cheng,et al.  Singularity analysis based on wavelet transform of fractal measures for identifying geochemical anomaly in mineral exploration , 2016, Comput. Geosci..

[11]  Bram van Ginneken,et al.  Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[12]  Takehisa Yairi,et al.  A review on the application of deep learning in system health management , 2018, Mechanical Systems and Signal Processing.

[13]  Sankar K. Pal,et al.  Self-organization for object extraction using a multilayer neural network and fuzziness measures , 1993, IEEE Trans. Fuzzy Syst..

[14]  Andrew F. Laine,et al.  Homomorphic wavelet shrinkage and feature emphasis for speckle reduction and enhancement of echocardiographic images , 1996, Medical Imaging.

[15]  Wei Shen,et al.  Multi-scale Convolutional Neural Networks for Lung Nodule Classification , 2015, IPMI.

[16]  Marina Cocchi,et al.  N‐way modeling for wavelet filter determination in multivariate image analysis , 2015 .

[17]  Ebrahim Ghaderpour,et al.  Least-squares cross-wavelet analysis and its applications in geophysical time series , 2018, Journal of Geodesy.

[18]  R F Rahmat,et al.  Advertisement scheduling on commercial radio station using genetics algorithm , 2018 .

[19]  Jia-Yush Yen,et al.  Integrating wavelet transformation with Markov random field analysis for the depth estimation of light-field images , 2017, IET Comput. Vis..

[20]  L. Zhang,et al.  Modeling Bird Species Richness at Multiple Spatial Scales Using Two-Dimensional Wavelet Analysis , 2015 .

[21]  Jamshid Dehmeshki,et al.  Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images , 2009, IEEE Transactions on Biomedical Engineering.

[22]  Witold Pedrycz,et al.  Fuzzy Wavelet Polynomial Neural Networks: Analysis and Design , 2017, IEEE Transactions on Fuzzy Systems.

[23]  Tae-Sun Choi,et al.  Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor , 2014, Comput. Methods Programs Biomed..

[25]  Dong Guo,et al.  Detection of vehicle wheels from images using a pseudo-wavelet filter for analysis of congested traffic , 2018, IET Image Process..

[26]  Hüseyin Göksu.  Ground moving target recognition using log energy entropy of wavelet packets , 2017 .

[27]  Ji Chen,et al.  A wavelet perspective on variabilities of hydrological processes in conjunction with geomorphic analysis over the Pearl River basin in South China , 2016 .

[28]  Hong Zhao,et al.  Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary Nodules , 2015, Journal of Digital Imaging.

[29]  Senthilkumar Krishnamurthy,et al.  An Automatic Computerized Model for Cancerous Lung Nodule Detection from Computed Tomography Images with Reduced False Positives , 2016, RTIP2R.

[30]  Wenhan Yang,et al.  Blind visual quality assessment for image super-resolution by convolutional neural network , 2018, Multimedia Tools and Applications.

[31]  Gang Hua,et al.  A convolutional neural network cascade for face detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Yang Liu,et al.  O-CNN , 2017, ACM Trans. Graph..

[33]  Bram van Ginneken,et al.  Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks , 2016, IEEE Transactions on Medical Imaging.

[34]  Jing Li,et al.  Robust Face Recognition Using the Deep C2D-CNN Model Based on Decision-Level Fusion , 2018, Sensors.

[35]  A. Volinsky,et al.  Application of the inverse wavelet transform on electrochemical current signals to demonstrate the essence of high‐frequency variations , 2010 .

[36]  A. Kubatiev,et al.  Application of wavelet analysis to detect dysfunction in cerebral blood flow autoregulation during experimental hyperhomocysteinaemia , 2018, Lasers in Medical Science.

[37]  Alexander Wong,et al.  Lung Nodule Classification Using Deep Features in CT Images , 2015, 2015 12th Conference on Computer and Robot Vision.

[38]  Jan Cornelis,et al.  A novel computer-aided lung nodule detection system for CT images. , 2011, Medical physics.