Interstitial lung disease classification using improved DenseNet

Interstitial Lung Disease (ILD) is one of the popular respiratory diseases. The correct diagnosis of ILD is beneficial to improve the effect of treatment for patients. This paper presents an improved DenseNet called small kernel DenseNet (SK-DenseNet) to improve ILD classification performance. According to the characteristics of HRCT features of lung disease, the SK-DenseNet network is more effective to extract high level and small pathological features for ILD classification. Our experiment results show that the proposed SK-DenseNet obtains an outstanding performance (~98.4%),which improves 5% performance compared with DenseNet. A comparative analysis with other CNNs, such as AlexNet, VGGNet, ResNet has also demonstrated that the effectiveness of SK-DenseNet in terms of classifying lung disease patterns is superior than those compared ones. The research has validated that using small convolution kernel is useful to improve the recognition efficiency when feature patterns are small.

[1]  Lauge Sørensen,et al.  Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns , 2010, IEEE Transactions on Medical Imaging.

[2]  Zhiyong Feng,et al.  Facial expression recognition via deep learning , 2014, 2014 International Conference on Smart Computing.

[3]  Henning Müller,et al.  Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography , 2010, Artif. Intell. Medicine.

[4]  Bernadette A. Thomas,et al.  Global, regional, and national age–sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013 , 2015, The Lancet.

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

[6]  Lin Wu,et al.  What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification , 2017, Pattern Recognit..

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

[8]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[9]  Alan D. Lopez,et al.  The Global Burden of Disease Study , 2003 .

[10]  David Dagan Feng,et al.  Feature-Based Image Patch Approximation for Lung Tissue Classification , 2013, IEEE Transactions on Medical Imaging.

[11]  Ye Xu,et al.  MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies , 2006, IEEE Transactions on Medical Imaging.

[12]  Dimitri Van De Ville,et al.  Near-Affine-Invariant Texture Learning for Lung Tissue Analysis Using Isotropic Wavelet Frames , 2012, IEEE Transactions on Information Technology in Biomedicine.

[13]  Lin Wu,et al.  Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval , 2017, IEEE Transactions on Image Processing.

[14]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Lin Wu,et al.  Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Bram van Ginneken,et al.  Computer-Aided Detection of Ground Glass Nodules in Thoracic CT Images Using Shape, Intensity and Context Features , 2011, MICCAI.

[17]  Richard Hans Robert Hahnloser,et al.  Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit , 2000, Nature.

[18]  Heng Huang,et al.  Locality-constrained Subcluster Representation Ensemble for lung image classification , 2015, Medical Image Anal..

[19]  T. King.  Clinical advances in the diagnosis and therapy of the interstitial lung diseases. , 2005, American journal of respiratory and critical care medicine.

[20]  Shiqing Zhang,et al.  Dimensionality reduction-based spoken emotion recognition , 2011, Multimedia Tools and Applications.

[21]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[22]  C B Carrington.  Interstitial lung disease (The 18th Aspen Lung Conference). Conference summary. , 1976, Chest.

[23]  Paul J. Friedman,et al.  American Thoracic Society/European Respiratory Society International Multidisciplinary Consensus Classification of the Idiopathic Interstitial Pneumonias. This joint statement of the American Thoracic Society (ATS), and the European Respiratory Society (ERS) was adopted by the ATS board of directors , 2002, American journal of respiratory and critical care medicine.

[24]  Lin Wu,et al.  Deep adaptive feature embedding with local sample distributions for person re-identification , 2017, Pattern Recognit..

[25]  Ronald M. Summers,et al.  Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[26]  E. Hoffman,et al.  Computer recognition of regional lung disease patterns. , 1999, American journal of respiratory and critical care medicine.

[27]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[28]  Carrington Cb.  Interstitial lung disease (The 18th Aspen Lung Conference). Conference summary. , 1976 .

[29]  Marios Anthimopoulos,et al.  Classification of interstitial lung disease patterns using local DCT features and random forest , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[31]  Dan J Stein,et al.  Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013 , 2015, The Lancet.

[32]  Antoine Geissbühler,et al.  Building a reference multimedia database for interstitial lung diseases , 2012, Comput. Medical Imaging Graph..

[33]  Shiqing Zhang,et al.  Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap , 2011, Sensors.

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

[35]  Xue Li,et al.  Deep Attention-Based Spatially Recursive Networks for Fine-Grained Visual Recognition , 2019, IEEE Transactions on Cybernetics.