A combination of FractalNet and CNN for Lung Nodule Classification

Lung Cancer is the fastest growing cancer around the world and is mostly diagnosed at an advanced stage. In order to prevent the delay in diagnosis, several medical imaging modalities like CT (Computed Tomography) has been used. So far researchers have used several machine learning architectures to classify the lung nodule captured in CT scans into benign or cancerous. In this paper, we use a deep learning architecture which combines fractalnet and CNN to classify the lung nodule into benign and malignant. The lung nodule classification was validated on LUNA dataset on which it achieved an accuracy of 94.06%, sensitivity of 97.52%, specificity of 86.76% and area under receiver operating characteristic (AUC) score of. 98, using the proposed architecture.

[1]  Wenqing Sun,et al.  Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis , 2017, Comput. Biol. Medicine.

[2]  Gregory Shakhnarovich,et al.  FractalNet: Ultra-Deep Neural Networks without Residuals , 2016, ICLR.

[3]  Bram van Ginneken,et al.  Automatic detection of large pulmonary solid nodules in thoracic CT images. , 2015, Medical physics.

[4]  Heung-Il Suk,et al.  Deep feature learning for pulmonary nodule classification in a lung CT , 2016, 2016 4th International Winter Conference on Brain-Computer Interface (BCI).

[5]  Aoxue Li,et al.  Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks , 2017, MICCAI.

[6]  He Ma,et al.  An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network , 2018, IEEE Journal of Biomedical and Health Informatics.

[7]  Wei Shen,et al.  Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification , 2017, Pattern Recognit..

[8]  Anselmo Cardoso de Paiva,et al.  Convolutional neural network-based PSO for lung nodule false positive reduction on CT images , 2018, Comput. Methods Programs Biomed..

[9]  Albert Chon,et al.  Deep Convolutional Neural Networks for Lung Cancer Detection , 2017 .

[10]  Allison M. Rossetto,et al.  Deep Learning for Categorization of Lung Cancer CT Images , 2017, 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE).

[11]  Yanning Zhang,et al.  NODULe: Combining constrained multi-scale LoG filters with densely dilated 3D deep convolutional neural network for pulmonary nodule detection , 2018, Neurocomputing.

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

[13]  Suane Pires P. da Silva,et al.  Lung Nodule Classification via Deep Transfer Learning in CT Lung Images , 2018, 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS).

[14]  Meijun Sun,et al.  Deep Learning Based Nodule Detection from Pulmonary CT Images , 2017, 2017 10th International Symposium on Computational Intelligence and Design (ISCID).

[15]  Sidong Liu,et al.  Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features , 2016, MCV/BAMBI@MICCAI.

[16]  Jing Chen,et al.  The effect of kernel size of CNNs for lung nodule classification , 2017, 2017 9th International Conference on Advanced Infocomm Technology (ICAIT).

[17]  Aimin Hao,et al.  Hybrid-feature-guided lung nodule type classification on CT images , 2018, Comput. Graph..

[18]  Tiantian Fang,et al.  A Novel Computer-Aided Lung Cancer Detection Method Based on Transfer Learning from GoogLeNet and Median Intensity Projections , 2018, 2018 IEEE International Conference on Computer and Communication Engineering Technology (CCET).

[19]  Shaohui Liu,et al.  Multi-path convolutional neural network for lung cancer detection , 2018, Multidimens. Syst. Signal Process..

[20]  Hao Chen,et al.  Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge , 2016, Medical Image Anal..

[21]  Ulas Bagci,et al.  TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[22]  Robert J. Gillies,et al.  Predicting Nodule Malignancy using a CNN Ensemble Approach , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[23]  A. Jemal,et al.  Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.

[24]  C. Zappa,et al.  Non-small cell lung cancer: current treatment and future advances. , 2016, Translational lung cancer research.

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

[26]  Masoom A. Haider,et al.  Discovery Radiomics via StochasticNet Sequencers for Cancer Detection , 2015, ArXiv.

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