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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Research Article

Brain Tumor Detection and Classification by Hybrid CNN-DWA Model Using MR Images

Author(s): Isselmou Abd El Kader*, Guizhi Xu, Zhang Shuai and Sani Saminu

Volume 17, Issue 10, 2021

Published on: 23 February, 2021

Page: [1248 - 1255] Pages: 8

DOI: 10.2174/1573405617666210224113315

Abstract

Objective: Medical image processing is an exciting research area. In this paper, we proposed new brain tumor detection and classification model using MR brain images to help the doctors in early detection and classification of the brain tumor with high performance and best accuracy.

Materials: The model was trained and validated using five databases, including BRATS2012, BRATS2013, BRATS2014, BRATS2015, and ISLES-SISS 2015.

Methods: The advantage of the hybrid model proposed is its novelty that is used for the first time; our new method is based on a hybrid deep convolution neural network and deep watershed auto-encoder (CNN-DWA) model. The method consists of six phases, first phase is input MR images, second phase is preprocessing using filter and morphology operation, third phase is matrix that represents MR brain images, fourth is applying the hybrid CNN-DWA, fifth is brain tumor classification, and detection, while sixth phase is the performance of the model using five values.

Results: The novelty of our hybrid CNN-DWA model showed the best results and high performance with accuracy around 98% and loss validation 0, 1. Hybrid model can classify and detect the tumor clearly using MR images; comparing with other models like CNN, DNN, and DWA, we discover that the proposed model performs better than the above-mentioned models.

Conclusion: Depending on the better performance of the proposed hybrid model, this helps in developing computer-aided system for early detection of brain tumors and helps the doctors to diagnose the patients better.

Keywords: Brain tumor, MRI, hybrid CNN-DWA, classification, detection, accuracy, and loss validation.

Graphical Abstract

[1]
Alhadidi B, Zu’bi MH, Suleiman HN. Mammogram breast cancer image detection using image processing functions. Inf Technol J 2007; 6(2): 217-21.
[http://dx.doi.org/10.3923/itj.2007.217.221]
[2]
Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng 2000; 2(1): 315-37.
[http://dx.doi.org/10.1146/annurev.bioeng.2.1.315] [PMID: 11701515]
[3]
Langote VB, Chaudhuri DS. Segmentation techniques for image analysis. Int J Adv Eng Res Stud 2012; 2(1): 252-5.
[4]
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88.https://arxiv.org/abs/1702.05747
[http://dx.doi.org/10.1016/j.media.2017.07.005] [PMID: 28778026]
[5]
Shen D, Wu G, Suk H-I. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017; 19: 221-48.
[http://dx.doi.org/10.1146/annurev-bioeng-071516-044442] [PMID: 28301734]
[6]
Suzuki K. Overview of deep learning in medical imaging. Radiological Phys Technol 2017; 10(3): 257-73.
[http://dx.doi.org/10.1007/s12194-017-0406-5] [PMID: 28689314]
[7]
Greenspan H, van Ginneken B, Summers RM. Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans Med Imaging 2016; 35(5): 1153-9.
[http://dx.doi.org/10.1109/TMI.2016.2553401]
[8]
Adams D. https://harzing.com/resources/publish-or-perish/windows
[9]
Shin H-C, Roth HR, Gao M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics, and transfer learning. IEEE Trans Med Imaging 2016; 35(5): 1285-98.
[http://dx.doi.org/10.1109/TMI.2016.2528162] [PMID: 26886976]
[10]
Zhou S, Chen Q, Wang X. Active deep networks for semi-supervised sentiment classification. Proceedings of 23rd International Conference on Computer Linguistics 1515-23.
[11]
Kuo W, Häne C, Yuh E, et al. Cost-sensitive active learning for intracranial hemorrhage detection. arXiv:1809.02882.
[http://dx.doi.org/10.1007/978-3-030-00931-1_82]
[12]
Gal Y, Islam R, Ghahramani Z. Deep Bayesian active learning with image data. Proc Int Conf Mach Learn. 1183-92.
[13]
Sener O, Savarese S. Active learning for convolutional neural networks: A core-set approach. https://arxiv.org/abs/1708.00489
[14]
Hiralal H, Menon HP. A survey of brain MRI image segmentation methods and the issues involved. Proc Int Symp Intell Syst Technol Appl. 245-59.
[http://dx.doi.org/10.1007/978-3-319-47952-1_19]
[15]
Yazdani S, Yusof R, Karimian A, et al. Image segmentation methods and applications in MRI brain images. IETE Tech Rev 2015; 32(6): 413-27.
[http://dx.doi.org/10.1080/02564602.2015.1027307]
[16]
Chen L, Lin L, Zuo W, et al. Learning a wavelet-like auto-encoder to accelerate deep neural networks. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). 6722-9.
[17]
Vincent P, Larochelle H, Bengio Y, et al. Extracting and composing robust features with denoising autoencoders. Proc 25th Int Conf Mach Learning (ICML) 1096-3.
[http://dx.doi.org/10.1145/1390156.1390294]
[18]
Kleć M, Koržinek D. Pre-trained deep neural network using sparse autoencoder and scattering wavelet transform for musical genre recognition. Comput Sci 2015; 16(2): 133-44.
[http://dx.doi.org/10.7494/csci.2015.16.2.133]
[19]
Lebedev V, Ganin Y, Rakhuba M, et al. Speeding-up convolutional neural networks using fine-tuned CP-decomposition. Proc Int Conf Learn Represent. 1-11.
[20]
Liou C-Y, Huang J-C, Yang W-C. Modeling word perception using the Elman network. Neurocomputing 2008; 71(16-18): 3150-7.
[http://dx.doi.org/10.1016/j.neucom.2008.04.030]
[21]
Liou C-Y, Cheng W-C, Liou J-W, et al. Autoencoder for words. Neurocomputing 2014; 139: 84-96.
[http://dx.doi.org/10.1016/j.neucom.2013.09.055]
[22]
Xiao Y, Wu J, Lin Z, Zhao X. A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data. Comput Methods Programs Biomed 2018; 166: 99-105.
[http://dx.doi.org/10.1016/j.cmpb.2018.10.004] [PMID: 30415723]
[23]
Dice LR. Measures of the amount of ecologic association between species. Ecology 1945; 26(3): 297-302.
[http://dx.doi.org/10.2307/1932409]
[24]
Available online: https://keras.io (accessed on 10 January 2019)
[25]
Chen L, Bentley P, Rueckert D. Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. Neuroimage Clin 2017; 15: 633-43.
[http://dx.doi.org/10.1016/j.nicl.2017.06.016] [PMID: 28664034]
[26]
Haeck T, Maes F, Suetens P. Automated model-based segmentation of ischemic stroke in MR images. 2015, 246-53
[27]
Larochelle H. A convolutional neural network approach to brain tumor segmentation. Proc Int Conf BrainLes 2015;. 195-208.
[28]
McKinley R, Häni L, Wiest R, Reyes M. Segmenting the ischemic penumbra: a decision forest approach with automatic threshold finding. Proc Int Conf BrainLes 2015;. 275: 83.
[http://dx.doi.org/10.1007/978-3-319-30858-6_24]
[29]
Mahmood Q, Basit A. Automatic Ischemic stroke lesion segmentation in multi-spectral MRI images using random forests classifier. Proc Int Conf BrainLes 2015;. 266: 74.
[http://dx.doi.org/10.1007/978-3-319-30858-6_23]
[30]
Abbasi S, Tajeripour F. Detection of a brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing 2017; 219: 526-35.
[http://dx.doi.org/10.1016/j.neucom.2016.09.051]
[31]
Cordier N, Menze B, Delingette H, et al. Patch-based segmentation of brain tissues. Proc MICCAI Challenge on Multimodal Brain Tumor Segmentation. Japan. 2013; pp. 2013; 6-17..
[32]
Dong H, Yang G, Liu F, et al. Automatic brain tumor detection and segmentation using U-net based fully convolutional networks. Proc Int Conf on Medical Image Understanding and Analysis. 506-17.
[http://dx.doi.org/10.1007/978-3-319-60964-5_44]
[33]
Pereira S, Pinto A, Alves V, Silva CA. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 2016; 35(5): 1240-51.
[http://dx.doi.org/10.1109/TMI.2016.2538465] [PMID: 26960222]
[34]
Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal 2017; 35: 18-31.
[http://dx.doi.org/10.1016/j.media.2016.05.004] [PMID: 27310171]
[35]
Kamnitsas K, Ledig C, Newcombe VFJ, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 2017; 36: 61-78.
[http://dx.doi.org/10.1016/j.media.2016.10.004] [PMID: 27865153]
[36]
Reza SM, Mays R, Iftekharuddin KM. Multifractal detrended texture feature for brain tumor classification. Proc Int Conf Medical Imaging Computer Aid-diagnosis. 9414-10.
[37]
Wu W, Chen AY, Zhao L, Corso JJ. Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features. Int J CARS 2014; 9(2): 241-53.
[http://dx.doi.org/10.1007/s11548-013-0922-7] [PMID: 23860630]
[38]
Bauer S, Fejes T, Slotboom J, Wiest R, Nolte LP, Reyes M. Segmentation of brain tumor images based on integrated hierarchical classification and regularization. Pro MICCAI BraTS Workshop. 11.
[39]
Huang M, Yang W, Wu Y, Jiang J, Chen W, Feng Q. Brain tumor segmentation based on local independent projection-based classification. IEEE Trans Biomed Eng 2014; 61(10): 2633-45.
[http://dx.doi.org/10.1109/TBME.2014.2325410] [PMID: 24860022]
[40]
Zikic D, Ioannou Y, Brown M, Criminisi A. Segmentation of brain tumor tissues with convolutional neural networks. Proceedings MICCAI-BRATS. 36-9.
[41]
Goetz M, Weber C, Bloecher J, Stieltjes B, Meinzer HP. Extremely randomized trees based brain tumor segmentation. Proceeding of BRATS challenge-MICCAI 2014.

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