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Classification of CT Scan Images of Lungs Using Deep Convolutional Neural Network with External Shape-Based Features

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Abstract

In this paper, a simplified yet efficient architecture of a deep convolutional neural network is presented for lung image classification. The images used for classification are computed tomography (CT) scan images obtained from two scientifically used databases available publicly. Six external shape-based features, viz. solidity, circularity, discrete Fourier transform of radial length (RL) function, histogram of oriented gradient (HOG), moment, and histogram of active contour image, have also been identified and embedded into the proposed convolutional neural network. The performance is measured in terms of average recall and average precision values and compared with six similar methods for biomedical image classification. The average precision obtained for the proposed system is found to be 95.26% and the average recall value is found to be 69.56% in average for the two databases.

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References

  1. Siegel RL, Miller KD, Jemal A: Cancer statistics, 2016. CA: a cancer journal for clinicians 66(1):7–30, 2016

    Google Scholar 

  2. De Azevedo-Marques, Paulo Mazzoncini, Arianna Mencattini, Marcello Salmeri, and Rangaraj M. Rangayyan, eds. "Medical Image Analysis and Informatics: Computer-Aided Diagnosis and Therapy.", CRC Press, Taylor and Francis, 2017.

  3. Purwar RK, Srivastava V: Recent advancements in detection of cancer using various soft computing techniques for MR images, in Progress of Advanced Computing and Intelligent Engineering. Singapore: Springer, 2018, pp. 99–108

    Google Scholar 

  4. Sluimer I, Schilham A, Prokop M, Van Ginneken B: Computer analysis of computed tomography scans of the lung: A survey. IEEE transactions on medical imaging 25(4):385–405, 2006

    Article  Google Scholar 

  5. Coppini G, Miniati M, Paterni M, Monti S, Ferdeghini EM: Computer-aided diagnosis of emphysema in COPD patients: Neural-network-based analysis of lung shape in digital chest radiographs. Medical engineering and physics 29(1):76–86, 2007

    Article  Google Scholar 

  6. Li, Xin, Leiting Chen, and Junyu Chen, "A visual saliency-based method for automatic lung regions extraction in chest radiographs.", In 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 162-165. IEEE, 2017.

  7. Abbasi S, Mokhtarian F, Kittler J: Curvature scale space image in shape similarity retrieval. Multimedia systems 7(6):467–476, 1999

    Article  Google Scholar 

  8. Tsochatzidis L, Zagoris K, Arikidis N, Karahaliou A, Costaridou L, Pratikakis I: Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach. Pattern Recognition 71:106–117, 2017

    Article  Google Scholar 

  9. Wang XH, Park SC, Zheng B: Assessment of performance and reliability of computer-aided detection scheme using content-based image retrieval approach and limited reference database. Journal of digital imaging 24(2):352–359, 2011

    Article  Google Scholar 

  10. Park, Yang Shin, Joon Beom Seo, Namkug Kim, Eun Jin Chae, Yeon Mok Oh, Sang Do Lee, Youngjoo Lee, and Suk-Ho Kang, "Texture-based quantification of pulmonary emphysema on high-resolution computed tomography: comparison with density-based quantification and correlation with pulmonary function test.", Investigative radiology, 43(6), 395-402, 2008.

  11. Nanni L, Lumini A, Brahnam S: Local binary patterns variants as texture descriptors for medical image analysis. Artificial intelligence in medicine 49(2):117–125, 2010

    Article  Google Scholar 

  12. Moura DC, Guevara López MA: An evaluation of image descriptors combined with clinical data for breast cancer diagnosis. International journal of computer assisted radiology and surgery 8(4):561–574, 2013

    Article  Google Scholar 

  13. Srivastava V, Purwar R: An extension of local mesh peak valley edge based feature descriptor for image retrieval in bio-medical images. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 7(1):77–89, 2018

    Article  Google Scholar 

  14. Pang S, Yu Z, Orgun MA: A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images. Computer methods and programs in biomedicine 140:283–293, 2017

    Article  Google Scholar 

  15. Karabulut, Esra Mahsereci, and Turgay Ibrikci. "Emphysema discrimination from raw HRCT images by convolutional neural networks." In 9th International Conference on Electrical and Electronics Engineering (ELECO), pp. 705-708, IEEE, 2015.

  16. Chung, Y. A., and Weng, W. H., "Learning deep representations of medical images using Siamese CNNs with application to content-based image retrieval", Computer Vision and Pattern Recognition, Cornell University,  arXiv:1711.08490, 2017.

  17. Moeskops P, Wolterink JM, van der Velden BH, Gilhuijs KG, Leiner T, Viergever MA, Išgum I: Deep learning for multi-task medical image segmentation in multiple modalities. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2016, pp. 478–486

    Google Scholar 

  18. Bermejo-Peláez, David, Raúl San José Estepar, and María J. Ledesma-Carbayo. "Emphysema classification using a multi-view convolutional network.", In 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 519-522. IEEE, 2018.

  19. Gao, M., Xu, Z., Lu, L., Harrison, A. P., Summers, R. M., and Mollura, D. J., "Holistic interstitial lung disease detection using deep convolutional neural networks: Multi-label learning and unordered pooling", Computer Vision and Pattern Recognition, Cornell University,  arXiv:1701.05616,  2017.

  20. Campo, Mónica Iturrioz, Javier Pascau, and Raúl San José Estépar. "Emphysema quantification on simulated X-rays through deep learning techniques." In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 273-276. IEEE, 2018.

  21. Nanni L, Ghidoni S, Brahnam S: Handcrafted vs. non-handcrafted features for computer vision classification. Pattern Recognition 71:158–172, 2017

    Article  Google Scholar 

  22. Srivastava Varun, Purwar RK, Jain Anchal, "A dynamic threshold-based local mesh ternary pattern technique for biomedical image retrieval. International Journal of Imaging Systems and Technology, Vol. 29(2), pp: 168-179, 2018.

  23. Liu H, Xu J, Wu Y, Guo Q, Ibragimov B, Xing L: Learning deconvolutional deep neural network for high resolution medical image reconstruction. Information Sciences 468:142–154, 2018

    Article  Google Scholar 

  24. Hua, Kai-Lung, Che-Hao Hsu, Shintami Chusnul Hidayati, Wen-Huang Cheng, and Yu-Jen Chen. "Computer-aided classification of lung nodules on computed tomography images via deep learning technique.", OncoTargets and therapy 8, 2015.

  25. Simonyan, Karen, and Andrew Zisserman, "Very deep convolutional networks for large-scale image recognition", Computer Vision and Pattern Recognition, Cornell University,  arXiv: 1409.1556, 2014.

  26. Wozniak P, Afrisal H, Esparza RG, Kwolek B: Scene recognition for indoor localization of mobile robots using deep CNN. In: International Conference on Computer Vision and Graphics. Cham: Springer, 2018, pp. 137–147

    Chapter  Google Scholar 

  27. Hoo-Chang S, Roth HR, Gao M et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging. 35(5):1285–1298, 2016. https://doi.org/10.1109/TMI.2016.2528162

    Article  PubMed Central  Google Scholar 

  28. Chan TF, Sandberg BY, Vese LA: Active contours without edges for vector-valued images. Journal of Visual Communication and Image Representation 11(2):130–141, 2000

    Article  Google Scholar 

  29. Nanni L, Paci M, Brahnam S, Ghidoni S: An ensemble of visual features for Gaussians of local descriptors and non-binary coding for texture descriptors. Expert Systems with Applications 82:27–39, 2017

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Visveswaraya Fellowship scheme for Ph.D. students by the Govt. of India for extending their support to carry out the research work. Also, the authors would like to extend their gratitude towards the editors and reviewers of Journal of Digital Imaging, Springer, for their help and support in revising this paper and to bring it into its present form.

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Authors and Affiliations

  1. University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Dwarka Sector 16C, New Delhi, 110075, India

    Varun Srivastava & Ravindra Kr. Purwar

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  1. Varun Srivastava
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  2. Ravindra Kr. Purwar
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Correspondence to Varun Srivastava.

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Srivastava, V., Purwar, R.K. Classification of CT Scan Images of Lungs Using Deep Convolutional Neural Network with External Shape-Based Features. J Digit Imaging 33, 252–261 (2020). https://doi.org/10.1007/s10278-019-00245-9

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  • DOI: https://doi.org/10.1007/s10278-019-00245-9

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