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DFD-Net: lung cancer detection from denoised CT scan image using deep learning

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Abstract

The availability of pulmonary nodules in CT scan image of lung does not completely specify cancer. The noise in an image and morphology of nodules, like shape and size has an implicit and complex association with cancer, and thus, a careful analysis should be mandatory on every suspected nodules and the combination of information of every nodule. In this paper, we introduce a “denoising first” two-path convolutional neural network (DFD-Net) to address this complexity. The introduced model is composed of denoising and detection part in an end to end manner. First, a residual learning denoising model (DR-Net) is employed to remove noise during the preprocessing stage. Then, a two-path convolutional neural network which takes the denoised image by DR-Net as an input to detect lung cancer is employed. The two paths focus on the joint integration of local and global features. To this end, each path employs different receptive field size which aids to model local and global dependencies. To further polish our model performance, in different way from the conventional feature concatenation approaches which directly concatenate two sets of features from different CNN layers, we introduce discriminant correlation analysis to concatenate more representative features. Finally, we also propose a retraining technique that allows us to overcome difficulties associated to the image labels imbalance. We found that this type of model easily first reduce noise in an image, balances the receptive field size effect, affords more representative features, and easily adaptable to the inconsistency among nodule shape and size. Our intensive experimental results achieved competitive results.

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References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel R L, Torre L A, Jemal A. Global cancer statistics 2018. A Cancer Journal for Clinicians, 2018, 68(6): 394–424

    Article  Google Scholar 

  2. National Lung Screening Trial Research Team. Reduced lung-cancer mortality with low-dose computed tomographic screening. New England Journal of Medicine, 2011, 365(5): 395–409

    Article  Google Scholar 

  3. Patz E F, Pinsky P, Gatsonis C, Sicks J D, Kramer B S, Tammemagi M C, Chiles C, Black W C, Aberle D R. Over diagnosis in low-dose computed tomography screening for lung Cancer. JAMA Internal Medicine, 2014, 174(2): 269–274

    Article  Google Scholar 

  4. Alvarez J M, Gevers T, LeCun Y, Lopez A M. Road scene segmentation from a single image. In: Proceedings of the 12th European Conference on Computer Vision. 2012, 376–389

  5. Liu Y, Gadepalli K, Norouzi M, Dahl G E, Kohlberger T, Boyko A, Venugopalan S, Timofeev A, Nelson P Q, Corrado G S, Hipp J D. Detecting cancer metastases on giga pixel pathology images. 2017, arXiv preprint arXiv: 1703. 02442

  6. Kuan K, Ravaut M, Manek G, Chen H, Lin J, Nazir B, Chen C, Howe T C, Zeng Z, Chandrasekhar V. Deep learning for lung cancer detection: tackling the kaggle data science bowl 2017 challenge. 2017, arXiv preprint arXiv: 1705. 09435

  7. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin P M, Larochelle H. Brain tumor segmentation with deep neural networks. Medical Image Analysis, 2017, 35:18–31

    Article  Google Scholar 

  8. Pereira S, Pinto A, Alves V, Silva C A. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging, 2016, 35(5): 1240–1251

    Article  Google Scholar 

  9. Jifara W, Jiang F, Rho S, Cheng M, Liu S. Medical image denoising using convolutional neural netwok: a residual learning approach. Journal of Super Computing, 2019, 75(2): 704–718

    Article  Google Scholar 

  10. Razzak M I, Naz S, Zaib A. Deep learning for medical image processing: overview, challenges and future. Classification in BioApps: Automation of Decision Making, 2017, 26: 323

    Article  Google Scholar 

  11. Clark M C, Hall L O, Goldgof D B, Velthuizen R, Murtagh F R, Silbiger M S. Automatic tumor segmentation using knowledge-based clustering. IEEE Transaction on Medical Imaging, 1998, 17(2): 187–201

    Article  Google Scholar 

  12. Lin D T, Yan C R. Lung nodules identification rules extraction with neural fuzzy network. In: Proceedings of the 9th International Conference on Neural Information Processing. 2002, 2049–2053

  13. Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of Advances in Neural Information Processing Systems. 2015, 91–99

  14. Redmon J, Farhadi A. Yolo: better, faster, stronger. 2016, arXiv preprint arXiv:1612.08242

  15. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y, Berg A C. SSD: single shot multi box detector. In: Proceedings of European Conference on Computer Vision. 2016, 21–37

  16. Ronghang H, Piotr D, Kaiming H, Trevor D, Ross G. Learning to segment everything. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018, 4233–4241

  17. Wu Y, He K. Group normalization. In: Proceedings of the European Conference on Computer Vision. 2018, 3–19

  18. Jiang X, Pang Y, Sun M, Li X. Cascaded sub patch networks for effective cnns. IEEE Transactions on Neural Networks and Learning Systems, 2017, 29(7): 2684–2694

    Google Scholar 

  19. Mobiny A, Van Nguyen H. Fast capsnet for lung cancer screening. In: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. 2018, 741–749

  20. Sori W J, Feng J, Liu S. Multi-path convolutional neural network for lung cancer detection. Multidimensional Systems and Signal Processing, 2019, 30(4): 1749–1768

    Article  MATH  Google Scholar 

  21. Gurcan M N, Sahiner B, Petrick N, Chan H P, Kazerooni E A, Cascade P N, Hadjiiski L. Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Medical Physics, 2002, 29(11): 2552–2558

    Article  Google Scholar 

  22. Chon A, Balachandar N, Lu P. Deep convolutional neural networks for lung cancer detection. Standford University, 2017

  23. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. 2015, 234–241

  24. Rao P, Pereira N A, Srinivasan R. Convolutional neural networks for lung cancer screening in computed tomography (CT) scans. In: Proceedings of International Conference on Contemporary Computing and Informatics. 2016, 489–493

  25. He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: surpassing human-level performance on imageNet classification. In: Proceedings of IEEE International Conference on Computer Vision. 2015, 1026–1034

  26. Kingma D P, Ba J. Adam: a method for stochastic optimization. 2014, arXiv preprint arXiv: 1412. 6980

  27. Vedaldi A, Lenc K. Matconvnet: convolutional neural networks for matlab. In: Proceedings of the 23rd ACM International Conference on Multimedia. 2015 689–692

  28. Liu C, Wechsler H. A shape-and texture-based enhanced fisher classifier for face recognition. IEEE Transaction on Image Process, 2001, 10(4): 598–608

    Article  MATH  Google Scholar 

  29. Yang J, Yang J Y. Generalized K-L transform based combined feature extraction. Pattern Recognition, 2002, 35(1): 295–297

    Article  MATH  Google Scholar 

  30. Yang J, Yang J Y, Zhang D, Lu J F. Feature fusion: parallel strategy vs. serial strategy. Pattern Recognition, 2003, 36(6): 1369–1381

    Article  MATH  Google Scholar 

  31. Sun Q S, Zeng S G, Liu Y, Heng P A, Xia D S. A new method of feature fusion and its application in image recognition. Pattern Recognition, 2005, 38(12): 2437–2448

    Article  Google Scholar 

  32. Schott J R. Principles of multivariate analysis: a user’s perspective. Journal of the American Statistical Association, 2002, 97(458): 657–659

    Article  Google Scholar 

  33. Haghighat M, Abdel-Mottaleb M, Alhalabi W. Discriminant correlation analysis: real-time feature level fusion for multi-modal bio-metric recognition. IEEE Transaction on Information Forensics Security, 2016, 11(9): 1984–1996

    Article  Google Scholar 

  34. Krizhevsky A, Sutskever I, Hinton G E. Image net classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems. 2012, 1097–1105

  35. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado G S, Davis A, Dean J, Devin M, Ghemawat S. TensorFlow: large-scale machine learning on heterogeneous distributed systems. 2016, arXiv Preprint arXiv: 1603. 04467

  36. Huang X, Shan J, Vaidya V. Lung nodules detection in CT using 3D Convolutional neural networks. In: Proceedings of the 14th IEEE International Symposium on Biomedical Imaging. 2017, 379–383

Download references

Acknowledgements

This work was partially funded by the national Key research and development program of China (2018YFC0806802 and 2018YFC0832105) and Bule Hora University of Ethiopia. We would like to acknowledge the editors and the anonymous reviewers whose important comments and suggestions led to greatly improved the manuscript.

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Correspondence to Worku J. Sori.

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Worku J. Sori received the BEd degrees in Mathematics from Mada Walabu University, Ethiopia in 2009, the Master degree in Applied Mathematics from Addis Ababa University, Ethiopia in 2011 and PhD degree in computer science and technology from Harbin Institute of Technology, China in 2019. He is now an assistant professor at the School of Electrical Engineering and Computing, department of Computer science and Engineering, at Adama Science and Technology University (ASTU), Ethiopia. His research interests include medical data processing, pattern recognition, image and video processing, and large data compression.

Jiang Feng received the BS, MS, and PhD degrees in computer science from Harbin Institute of Technology (HIT), China in 2001, 2003, and 2008, respectively. He is now a full professor in the Department of Computer Science, Harbin Institute of Technology and a visiting scholar in the School of Electrical Engineering, Princeton University. His research interests include computer vision, pattern recognition and image and video processing.

Arero W. Godana received the BSc degree from Bule Hora University, Ethiopia, and MSc degrees in computer science from Harbin Institute of Technology, China in 2016 and 2019, respectively. He is now a Lecturer at Arsi University, Ethiopia. His research interests include Satellite image processing, pattern recognition and image and video processing.

Shaohui Liu received the BS. MS. and PhD degrees in computer science from Harbin Institute of Technology (HIT), China in 2000, 2002, and 2007, respectively. He is now an Associated Professor in the Department of Computer Science, Harbin Institute of Technology, China. His research interests include data compression, pattern recognition and image and video processing.

Demissie J. Gelmecha received the BSc degree in physics from Haramaya University, Ethiopia in 2006, the MSc degree in physical electronics engineering from Central China Normal University, Institute of Technology, China in 2011 and PhD degree in optical Engineering from Harbin Institute of Technology, China in 2018. Currently, he is an Assistant Professor of Optical Engineering at the Department of Electronics and Communication Engineering, School of Electrical Engineering and Computing at Adama Science and Technology University (ASTU), Ethiopia. He is also serving the University as Dean of Academic Staff Affairs. His current research interests include fiber-optic communications, non-linear chiral fibers for optical communications and new optical devices, and full-duplex communication systems.

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Sori, W.J., Feng, J., Godana, A.W. et al. DFD-Net: lung cancer detection from denoised CT scan image using deep learning. Front. Comput. Sci. 15, 152701 (2021). https://doi.org/10.1007/s11704-020-9050-z

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