DR-UNET: A Hybrid Model For Classification of G lioma using Transfer Learning on MR Images

DR-UNET: A Hybrid Model For Classification of G lioma using Transfer Learning on MR Images
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© 2021 by IJETT Journal
Volume-69 Issue-10
Year of Publication : 2021
Authors : Dheeraj D, Prasantha H S
DOI :  10.14445/22315381/IJETT-V69I10P218

How to Cite?

Dheeraj D, Prasantha H S, "DR-UNET: A Hybrid Model For Classification of G lioma using Transfer Learning on MR Images," International Journal of Engineering Trends and Technology, vol. 69, no. 10, pp. 146-150, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I10P218

Abstract
In medical imaging, one of the tough tasks is the classification of tumors present in the human brain. The work concentrates on the detection of the exact infected location in the human brain that consists of tumor and provide suitable techniques to administer treatment for the same. To achieve this objective, although there are various deep learning techniques employed by different researchers, an attempt has been made in this work to diagnose the existence of tumors using transfer learning. The experimentation has been carried out on the standard benchmark dataset-BraTs 2018. A hybrid model has been developed in this work for classifying the tumor as benign or malignant. The hybrid model is developed using depth-wise convolutions instead of the traditional approach in combination with the residual block being introduced in the final layer into the modified U-Net model deployed using a pre-trained VGG-16 model for classification. This hybrid model was then fine-tuned by varying certain vital hyperparameters to obtain an accuracy of about 92.30%.

Keywords
Transfer Learning, Deptwise-CNN, VGG-16, Semantic Segmentation, Residual U-Net, Fine Tuning.

Reference
[1] Weller M, Wick W, Aldape K, Brada M, Berger M, Pfister SM, Nishikawa R, Rosenthal M, Wen PY, Stupp R, Reifenberger G. Glioma. Nat Rev Dis Primers. (2015) Jul 16;1:15017. doi: 10.1038/nrdp.2015.17. PMID: 27188790.
[2] Louis, D.N.; Holland, E.C.; Cairncross, J.G. Glioma classification: A molecular reappraisal. Am. J. Pathol. (2001), 159, 779. [CrossRef].
[3] Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella- Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. ActaNeuropathol. 131(6) (2016) 803-20. doi: 10.1007/s00401-016- 1545-1. Epub 2016 May 9. PMID: 27157931.
[4] Valverde JM, Imani V, Abdollahzadeh A, De Feo R, Prakash M, Ciszek R, Tohka J. Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review. Journal of Imaging. 7(4) (2021) 66. https://doi.org/10.3390/jimaging7040066.
[5] Dheeraj D, Prasantha H S, Analysis Of Performance Of Ensemble Based Machine Learning Algorithms For Classification Of Glioma Using MR Images, Turkish Journal of Computer and Mathematics Education, 12(12) (2021) 4101-4108.
[6] Anil Kumar B , Dr.P Rajesh Kumar, Tumor Classification using Block wise fine tuning and Transfer learning of Deep Neural Network and KNN classifier on MR Brain Images, International Journal of Emerging Trends in Engineering Research, 8(2) (2020) 574-583.
[7] Ronneberger, O.; Fischer, P.; Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. Form. Asp. Compon. Softw, 9351 (2015) 234–241.
[8] Dheeraj D., Prasantha H.S., Study of Machine Learning vs Deep Learning Algorithms for Detection of Tumor in Human Brain,International Journal of Computer Sciences and Engineering, 8(1) 57-63.
[9] LeCun, Y.; Denker, J.S.; Solla, S.A. Optimal Brain Damage. Adv. Neural Inf. Process. Syst., 2 (1990) 598–605.
[10] Hassibi, B.; Stork, D.G. Second Order Derivaties for Network Prunning: Optimal Brain Surgeon. Available online:https://authors.library.caltech.edu/54983/3/647-second-orderderivatives- for-networkpruning- optimal-brain-surgeon.pdf.
[11] Alvarez, J.M.; Salzmann, M. Compression-aware Training of Deep networks 2017. Available online: http: //papers.nips.cc/paper/6687- compression-aware-training-of-deep-networks
[12] Han, S.; Mao, H.; Dally,W.J. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization, and Human Coding. Available online: https://arxiv.org/abs/1510.00149
[13] Howard, A.G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; Adam, H. MobileNets: E cient Convolutional Neural Networks for Mobile Vision Applications. Available online: https://arxiv.org/abs/1704.04861
[14] Zhang, X.; Zhou, X.; Lin, M.; Sun, J. ShueNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018, Salt Lake City, AL, USA, 18–22 June (2018) 6848–6856
[15] Qin, Z.; Zhang, Z.; Chen, X.; Peng, Y. FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy. 2018. Available online: https://ieeexplore.ieee.org/abstract/document/8451355
[16] Zhang W. et al., Deep convolutional neural networks for multimodality isointense infant brain image segmentation, Neuroimage.
[17] D. Nie, L. Wang, Y. Gao, and D. Sken, Fully convolutional networks for multi-modality isointense infant brain image segmentation, in Proceedings—International Symposium on Biomedical Imaging.
[18] A. De Brébisson and G. Montana, Deep neural networks for anatomical brain segmentation, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[19] Moeskops P., Viergever M. A., Mendrik A. M., De Vries L. S., Benders M. J. N. L., and Isgum I., Automatic Segmentation of MR Brain Images with a Convolutional Neural Network, IEEE Trans Med. Imaging.
[20] Ronneberger O., Fischer P., and Brox T., U-net: Convolutional networks for biomedical image segmentation, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
[21] Deepak S, Ameer PM. Brain tumor classification using deep CNN features via transfer learning. Comput. Biol. Med. 2019;111:103345. doi: 10.1016/j.compbiomed.2019.103345.
[22] Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv:1502.03167 [cs].
[23] He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition, arXiv:1512.03385 [cs].
[24] To?açar M, Ergen B, Cömert Z. BrainMRNet: brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Med. Hypotheses. (2020) 134:109531. doi: 10.1016/j.mehy.2019.109531.
[25] Sharif MI, Li JP, Khan MA, Saleem MA. Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recogn. Lett. 129 (2020) 181–189. doi: 10.1016/j.patrec.2019.11.019.
[26] Bernal J, et al. Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif. Intell. Med., 95 (2019) 64–81. doi: 10.1016/j.artmed.2018.08.008.
[27] Swati ZNK, et al. Brain tumor classification for MR images using transfer learning and fine-tuning. Comput. Med. Graph., 75(2019) 34–46. doi: 10.1016/j.compmedimag.2019.05.001.
[28] Talo, Muhammed, Ulas Baran Baloglu, Özal Y?ld?r?m, and U. Rajendra Acharya. Application of deep transfer learning for automated brain abnormality classification using MR images. Cognitive Systems Research 54 (2019) 176-188.
[29] Yao, W.; Zeng, Z.; Lian, C.; Tang, H. Pixel-wise regression using UNet and its application on pansharpening. Neurocomputing, 312 (2018) 364–371.
[30] Hesamian, M.H.; Jia, W.; He, X.; Kennedy, P. Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges. J. Digit. Imaging , 32 (2019) 582-596.
[31] Y. Fu, T. M. Hospedales, T. Xiang, and S. Gong, Transductive multiview zero-shot learning, IEEE Trans. Pattern Anal. Mach. Intell., 37(11) 2332–2345.
[32] Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, 3D U-net: Learning dense volumetric segmentation from sparse annotation, in Proc. MICCAI, (2016) 424–432.
[33] N. Tajbakhsh et al., Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Trans. Med. Imag., 35(5) (2016) 1299–1312.
[34] Brain tumor dataset. https://figshare.com/articles/brain_tumor_dataset/1512427. Accessed 17 Feb 2020
[35] N. Tajbakhsh et al., Convolutional neural networks for medical image analysis: Full training or fine tuning?, IEEE Trans. Med. Imag., 35(5)(2016) 1299–1312.
[36] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs, IEEE Trans. Pattern Anal. Mach. Intell., 40(4) 834–848.