ISSN E 2409-2770
ISSN P 2521-2419

A Modified Memory-Efficient U-Net for Segmentation of Polyps

Asif Ahmad, Noor Badshah, Mahmood Ul Hassan

Vol. 8, Issue 04, PP. 132-137, April 2021


Keywords: Deep Learning; Deep Neural Network; Image Segmentation; U-Net

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Colorectal cancer, caused by an unusual growth of tissues in a body called polyp, is the third most prevailing cancer worldwide and remained the second most cause of deaths by cancer in 2020. Early stage detection of the cancer can prevent the deaths. Computer Aided Diagnosis (CAD) system could be a major breakthrough for early detection of the cancer. The system uses image processing techniques. Among the image processing techniques segmentation has a great value. The diagnostic process results are highly dependent on the accuracy of performed segmentation. Nowadays, many supervised and unsupervised techniques are used for the task of segmentation. Deep neural networks have outperformed other state-of-the-art approaches for the task. In this paper, we present an end-to-end deep neural network for segmentation of polyps in images. The network is modified version of the U-Net architecture. The network being much more memory efficient than the U-Net architecture, inferences segmentation of the images more accurate than the U-Net. We reduce number of layers of the U-Net architecture both in the en- coding and decoding path, and introduce residual blocks and batch normalization in the encoding path to prevent learning of redundant features, to avoid over-fitting and to accelerate the training process, and in the decoding path to avoid gradient vanishing issue in long dependence of the neural network during training we use bi-directional long short term memory network with batch normalization. We train and validate the network on Kvasir dataset for the task. The network accurately segments the polyp part in the images with 92.46% test accuracy.

  1. Asif Ahmad,, Department of Basic Sciences, University of Engineering and Technology Peshawar, Pakistan.
  2. Noor Badshah,, Department of Basic Sciences, University of Engineering and Technology Peshawar, Pakistan.
  3. Mahmood Ul Hassan,, Department of Basic Sciences, University of Engineering and Technology Peshawar, Pakistan.

Asif Ahmad Noor Badshah Mahmood Ul Hassan “A Modified Memory-Efficient U-Net for Segmentation of Polyps” International Journal of Engineering Works Vol. 8 Issue 04 PP. 132-137 April 2021

  1. C. Dromain, B. Boyer, R. Ferre, S. Canale, S. Delaloge, and C. Balleyguier, “Computed-aided diagnosis (cad) in the detection of breast cancer,” European journal of radiology, vol. 82, no. 3, pp. 417–423, 2013.
  2. T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Transactions on image processing, vol. 10, no. 2, pp. 266–277, 2001.
  3. L. Wang, L. He, A. Mishra, and C. Li, “Active contours driven by local gaussian distribution fitting energy,” Signal Processing, vol. 89, no. 12, pp. 2435–2447, 2009.
  4. O. J. Tobias and R. Seara, “Image segmentation by histogram thresholding using fuzzy sets,”
  5. A. Ahmad, N. Badshah, and H. Ali, “A fuzzy          variational model for segmentation of images having intensity inhomogeneity and slight texture,” Soft Computing, pp. 1–16, 2020.
  6. C. W. Chen, J. Luo, and K. J. Parker, “Image segmentation via adaptive k-mean clus- tering and knowledge-based morphological operations with biomedical applications,” IEEE transactions on image processing, vol. 7, no. 12, pp. 1673–1683, 1998.
  7. S. Z. Oo and A. S. Khaing, “Brain tumor detection and segmentation using watershed seg- mentation and morphological operation,” International Journal of Research in Engineering and Technology, vol. 3, no. 03, pp. 367–374, 2014.
  8. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical im- age segmentation,” in International Conference on Medical image computing and computer- assisted intervention, pp. 234–241, Springer, 2015.
  9. R. Azad, M. Asadi-Aghbolaghi, M. Fathy, and S. Escalera, “Bi-directional convlstm u-net with densley connected convolutions,” in Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 0–0, 2019.
  10. L. Zhang, A. Liu, J. Xiao, and P. Taylor, “Dual encoder fusion u-net (defu-net) for cross- manufacturer chest x-ray segmentation,” arXiv preprint arXiv:2009.10608, 2020.
  11. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural computation, vol. 1, no. 4, pp. 541–551, 1989.
  12. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolu- tional neural networks,” in Advances in neural information processing systems, pp. 1097– 1105, 2012.
  13. D. Zikic, Y. Ioannou, M. Brown, and A. Criminisi, “Segmentation of brain tumor tissues with convolutional neural networks,” Proceedings MICCAI-BRATS, pp. 36–39, 2014.
  14. S. Pereira, A. Pinto, V. Alves, and C. A. Silva, “Brain tumor segmentation using convolutional neural networks in mri images,” IEEE Transactions on Medical Imaging, vol. 35, pp. 1240–1251, May 2016.
  15. H. Dong, G. Yang, F. Liu, Y. Mo, and Y. Guo, “Automatic brain tumor detection and seg- mentation using u-net based fully convolutional networks,” in annual conference on medical image understanding and analysis, pp. 506–517, Springer, 2017.
  16. P.-Y. Kao, J. W. Chen, and B. Manjunath, “Improving 3d u-net for brain tumor segmenta- tion by utilizing lesion prior,” arXiv preprint arXiv:1907.00281, 2019.
  17. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770– 778, 2016.
  18. S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by re- ducing internal covariate shift,” arXiv preprint arXiv:1502.03167, 2015.
  19. H. Song, W. Wang, S. Zhao, J. Shen, and K.-M. Lam, “Pyramid dilated deeper convlstm for video salient object detection,” in Proceedings of the European conference on computer vision (ECCV), pp. 715–731, 2018.
  20. M. I. Jordan, “Attractor dynamics and parallelism in a connectionist sequential machine,” in Artificial neural networks: concept learning, pp. 112–127, 1990.