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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 MR Image Classification for Glioma Tumor detection using Deep Convolutional Neural Network Features

Author(s): Ghazanfar Latif*, D.N.F. Awang Iskandar, Jaafar Alghazo and M. Mohsin Butt

Volume 17, Issue 1, 2021

Published on: 11 March, 2020

Page: [56 - 63] Pages: 8

DOI: 10.2174/1573405616666200311122429

Price: $65

Abstract

Background: Detection of brain tumor is a complicated task, which requires specialized skills and interpretation techniques. Accurate brain tumor classification and segmentation from MR images provide an essential choice for medical treatments. Different objects within an MR image have similar size, shape, and density, which makes the tumor classification and segmentation even more complex.

Objective: Classification of the brain MR images into tumorous and non-tumorous using deep features and different classifiers to get higher accuracy.

Methods: In this study, a novel four-step process is proposed; pre-processing for image enhancement and compression, feature extraction using convolutional neural networks (CNN), classification using the multilayer perceptron and finally, tumor segmentation using enhanced fuzzy cmeans method.

Results: The system is tested on 65 cases in four modalities consisting of 40,300 MR Images obtained from the BRATS-2015 dataset. These include images of 26 Low-Grade Glioma (LGG) tumor cases and 39 High-Grade Glioma (HGG) tumor cases. The proposed CNN feature-based classification technique outperforms the existing methods by achieving an average accuracy of 98.77% and a noticeable improvement in the segmentation results are measured.

Conclusion: The proposed method for brain MR image classification to detect Glioma Tumor detection can be adopted as it gives better results with high accuracies.

Keywords: Tumor detection, MR image classification, CNN Features, glioma Tumor, tumor Segmentation, brain MRI.

Graphical Abstract

[1]
Assayag O, Grieve K, Devaux B, et al. Imaging of non-tumorous and tumorous human brain tissues with full-field optical coherence tomography. Neuroimage Clin 2013; 2: 549-57.
[http://dx.doi.org/10.1016/j.nicl.2013.04.005] [PMID: 24179806]
[2]
Louis DN, Ohgaki H, Wiestler OD, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 2007; 114(2): 97-109.
[http://dx.doi.org/10.1007/s00401-007-0243-4] [PMID: 17618441]
[3]
Powers JA. Magnetic resonance imaging in marrow diseases. Clin Orthop Relat Res 1986; (206): 79-85.
[PMID: 3708996]
[4]
Lord SJ, Lei W, Craft P, et al. A systematic review of the effectiveness of magnetic resonance imaging (MRI) as an addition to mammography and ultrasound in screening young women at high risk of breast cancer. Eur J Cancer 2007; 43(13): 1905-17.
[http://dx.doi.org/10.1016/j.ejca.2007.06.007] [PMID: 17681781]
[5]
Gordillo N, Montseny E, Sobrevilla P. State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 2013; 31(8): 1426-38.
[http://dx.doi.org/10.1016/j.mri.2013.05.002] [PMID: 23790354]
[6]
Kodratoff Y, Michalski RS. Machine learning: an artificial intelligence approach. 2014; Vol. 3.
[7]
LeCun Y, Bengio Y, Hinton G. Deep learning Nature 2015; 521(7553): 436-4.
[8]
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. 2012; pp. 1097-105.
[9]
Menze BH, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 2015; 34(10): 1993-2024.
[http://dx.doi.org/10.1109/TMI.2014.2377694] [PMID: 25494501]
[10]
Kalaiselvi T, Nagaraja P. An automatic segmentation of brain tumor from MRI scans through wavelet transformations. Int J. Image. Graphics and Signal Processing 2016; 8(11): 59.
[http://dx.doi.org/10.5815/ijigsp.2016.11.08]
[11]
Hsieh KL, Lo CM, Hsiao CJ. Computer-aided grading of gliomas based on local and global MRI features. Comput Methods Programs Biomed 2017; 139: 31-8.
[http://dx.doi.org/10.1016/j.cmpb.2016.10.021] [PMID: 28187893]
[12]
Havaei M, Larochelle H, Poulin P, Jodoin PM. Within-brain classification for brain tumor segmentation. Int J CARS 2016; 11(5): 777-88.
[http://dx.doi.org/10.1007/s11548-015-1311-1] [PMID: 26530300]
[13]
Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw 2015; 61: 85-117.
[http://dx.doi.org/10.1016/j.neunet.2014.09.003] [PMID: 25462637]
[14]
Latif G, Alghazo J, Alzubaidi L, Naseer MM, Alghazo Y. Deep convolutional neural network for recognition of unified multi-language handwritten numerals. 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR). 90-5.
[http://dx.doi.org/10.1109/ASAR.2018.8480289]
[15]
Zhao Z, Jiao L, Zhao J, Gu J, Zhao J. Discriminant deep belief network for high-resolution SAR image classification. Pattern Recognit 2017; 61: 686-701.
[http://dx.doi.org/10.1016/j.patcog.2016.05.028]
[16]
Zikic D, Glocker B, Konukoglu E, et al. Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. International Conference on Medical Image Computing and Computer-Assisted Intervention. 369-76.
[http://dx.doi.org/10.1007/978-3-642-33454-2_46]
[17]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556 2014 Sep; 4.
[18]
Niu XX, Suen CY. A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recognit 2012; 45(4): 1318-25.
[http://dx.doi.org/10.1016/j.patcog.2011.09.021]
[19]
Ciresan DC, Meier U, Gambardella LM, Schmidhuber J. Convolutional neural network committees for handwritten character classification. 2011 International Conference on Document Analysis and Recognition. 1135-9.
[http://dx.doi.org/10.1109/ICDAR.2011.229]
[20]
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]
[21]
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]
[22]
Maas AL, Hannun AY, Ng AY. Rectifier nonlinearities improve neural network acoustic models. Proc icml 2013 Jun 16; 30(1): 3.
[23]
Tariq M, Siddiqi AA, Narejo GB, Andleeb S. A Cross Sectional Study of Tumors Using Bio-Medical Imaging Modalities. Curr Med Imaging Rev 2019; 15(1): 66-73.
[http://dx.doi.org/10.2174/1573405613666170614081434] [PMID: 31964329]
[24]
Hussain L, Saeed S, Awan IA, Idris A, Nadeem MSA, Chaudhry QU. Detecting Brain Tumor using Machines Learning Techniques Based on Different Features Extracting Strategies. Curr Med Imaging Rev 2019; 15(6): 595-606.
[http://dx.doi.org/10.2174/1573405614666180718123533] [PMID: 32008569]
[25]
Saba T, Khan MA, Rehman A, Marie-Sainte SL. Region extraction and classification of skin cancer: A heterogeneous framework of deep CNN features fusion and reduction. J Med Syst 2019; 43(9): 289.
[http://dx.doi.org/10.1007/s10916-019-1413-3] [PMID: 31327058]
[26]
Latif G, Iskandar DA, Alghazo JM, Mohammad N. Enhanced MR image classification using hybrid statistical and wavelets features. IEEE Access 2018; 7: 9634-44.
[http://dx.doi.org/10.1109/ACCESS.2018.2888488]
[27]
Latif G, Iskandar DA, Alghazo J, Jaffar A. Improving brain MR image classification for tumor segmentation using phase congruency. Current Medical Imaging 2018; 14(6): 914-22.
[http://dx.doi.org/10.2174/1573405614666180402150218]
[28]
Choi BK, Madusanka N, Choi HK, et al. Convolutional neural network-based MR image analysis for alzheimer’s disease classification. Curr Med Imaging Rev 2020; 16(1): 27-35.
[http://dx.doi.org/10.2174/1573405615666191021123854] [PMID: 31989891]
[29]
Khan AA, Narejo GB. Analysis of abdominal computed tomography images for automatic liver cancer diagnosis using image processing algorithm. Curr Med Imaging Rev 2019; 15(10): 972-82.
[http://dx.doi.org/10.2174/1573405615666190716122040] [PMID: 32008524]
[30]
Latif G, Iskandar DA, Alghazo J. Multiclass brain tumor classification using region growing based tumor segmentation and ensemble wavelet features. Proceedings of the 2018 International Conference on Computing and Big Data. 67-72.
[http://dx.doi.org/10.1145/3277104.3278311]
[31]
Saygili A, Albayrak S. Knee Meniscus Segmentation and Tear Detection from MRI: A Review. Curr Med Imaging Rev 2020; 16(1): 2-15.
[http://dx.doi.org/10.2174/1573405614666181017122109] [PMID: 31989889]
[32]
Krissian K, Aja-Fernández S. Noise-driven anisotropic diffusion filtering of MRI. IEEE Trans Image Process 2009; 18(10): 2265-74.
[http://dx.doi.org/10.1109/TIP.2009.2025553] [PMID: 19546041]
[33]
Jaffar MA, Zia S, Latif G, et al. Anisotropic diffusion based brain MRI segmentation and 3D reconstruction. Int J of Comput Int Sys 2012; 5(3): 494-504.
[http://dx.doi.org/10.1080/18756891.2012.696913]
[34]
Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990; 12(7): 629-39.
[http://dx.doi.org/10.1109/34.56205]
[35]
Donahue J, Jia Y, Vinyals O, et al. Decaf: A deep convolutional activation feature for generic visual recognition. International conference on machine learning 2014 Jan 27; 647-55.

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