Computer-Aided Detection of Lung Nodules in Chest X-Rays using Deep Convolutional Neural Networks

Chest X-Rays are most accessible medical imaging technique for diagnosing abnormalities in the heart and lung area. Automatically detecting these abnormalities with high accuracy could greatly enhance real world diagnosis processes. In this study we aim to improve the accuracy of convolutional deep learning by using Laplacian of Gaussian filtering. In this study, we have used the publicly available Japanese Society of Radiological Technology dataset including 247 radiograms. For improving the performance of convolutional neural networks we used LoG filter and also we used an advanced version of AlexNet and GoogleNet to compare our results. The results indicated that, convolutional neural network with Laplacian of Gaussian filter model produced the best results with 82.43% accuracy. Convolutional neural network with Laplacian of Gaussian filter model is followed by convolutional neural network with an accuracy of 72.97%, followed by GoogleNet model with an accuracy of 68.92%. Out of the four model types utilized, the AlexNet model produced the lowest accuracy with a value of 64.86%. The results obtained here demonstrate that the pre-processing technique like Laplacian of Gaussian filter can improve the accuracy.

[1]  P. Lakhani,et al.  Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. , 2017, Radiology.

[2]  Andrew Y. Ng,et al.  CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning , 2017, ArXiv.

[3]  Gregory Shakhnarovich,et al.  Feedforward semantic segmentation with zoom-out features , 2014, CVPR.

[4]  한보형,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015 .

[5]  Dinggang Shen,et al.  Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis , 2014, MICCAI.

[6]  Hayit Greenspan,et al.  Chest pathology detection using deep learning with non-medical training , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[7]  Ronald M. Summers,et al.  Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation , 2015, IEEE Transactions on Medical Imaging.

[8]  P. Huang,et al.  Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study. , 2018, Radiology.

[9]  Marios Anthimopoulos,et al.  Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.

[10]  Gérard G. Medioni,et al.  Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[14]  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).

[15]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[16]  Ronald M. Summers,et al.  Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  K. Doi,et al.  Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules. , 2000, AJR. American journal of roentgenology.

[18]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[19]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[20]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[22]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[23]  Peng Gang,et al.  Dimensionality reduction in deep learning for chest X-ray analysis of lung cancer , 2018, 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI).

[24]  Iasonas Kokkinos,et al.  Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.

[25]  Shirley Dex,et al.  JR 旅客販売総合システム（マルス）における運用及び管理について , 1991 .

[26]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.