Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms

• Autorzy: Kabir Anaraki Amin , Ayati Moosa , Kazemi Foad

Identyfikatory

DOI

10.1016/j.bbe.2018.10.004

• Języki publikacji: EN

Abstrakty

EN

Gliomas are the most common type of primary brain tumors in adults and their early detection is of great importance. In this paper, a method based on convolutional neural networks (CNNs) and genetic algorithm (GA) is proposed in order to noninvasively classify different grades of Glioma using magnetic resonance imaging (MRI). In the proposed method, the architecture (structure) of the CNN is evolved using GA, unlike existing methods of selecting a deep neural network architecture which are usually based on trial and error or by adopting predefined common structures. Furthermore, to decrease the variance of prediction error, bagging as an ensemble algorithm is utilized on the best model evolved by the GA. To briefly mention the results, in one case study, 90.9 percent accuracy for classifying three Glioma grades was obtained. In another case study, Glioma, Meningioma, and Pituitary tumor types were classified with 94.2 percent accuracy. The results reveal the effectiveness of the proposed method in classifying brain tumor via MRI images. Due to the flexible nature of the method, it can be readily used in practice for assisting the doctor to diagnose brain tumors in an early stage.

Słowa kluczowe

EN

brain tumor; magnetic resonance imaging; medical image classification; convolutional neural network; genetic algorithm; bagging ensemble algorithm

PL

guz mózgu; rezonans magnetyczny; sieć neuronowa konwolucyjna; algorytm genetyczny

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 1
• Strony: 63--74
• Opis fizyczny: Bibliogr. 40 poz., rys., tab., wykr.

Twórcy

autor

Kabir Anaraki Amin

• Advanced Instrumentation Lab, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran

autor

Ayati Moosa

• School of Mechanical Engineering, College of Engineering, University of Tehran, P.O.B. 11155-4563, Tehran, Iran

autor

Kazemi Foad

• Iran University of Medical Sciences, Tehran, Iran

Bibliografia

• [1] Wen PY, Kesari S. Malignant gliomas in adults. N Engl J Med 2008;359(5):492–507.
• [2] Louis DN, Perry A, Reifenberger G, Von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 2016;131 (6):803–20.
• [3] Laws ER, Ezzat S, Asa SL, Rio LM, Michel L, Knutzen R. Pituitary disorders: diagnosis and management. John Wiley & Sons; 2013.
• [4] Black PM. Brain tumors. N Engl J Med 1991;324(22):1555–64.
• [5] Kelly PJ. Gliomas: survival, origin and early detection. Surg Neurol Int 2010;1.
• [6] Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017.
• [7] Lo C-S, Wang C-M. Support vector machine for breast MR image classification. Comput Math Appl 2012;64(5):1153–62.
• [8] Trigui R, Mitéran J, Walker PM, Sellami L, Hamida AB. Automatic classification and localization of prostate cancer using multi-parametric MRI/MRS. Biomed Signal Process Control 2017;31:189–98.
• [9] Rasti R, Teshnehlab M, Phung SL. Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks. Pattern Recogn 2017;72:381–90.
• [10] Chaplot S, Patnaik L, Jagannathan N. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control 2006;1(1):86–92.
• [11] El-Dahshan E-SA, Hosny T, Salem A-BM. Hybrid intelligent techniques for MRI brain images classification. Digit Signal Process 2010;20(2):433–41.
• [12] Zhang Y, Dong Z, Wu L, Wang S. A hybrid method for MRI brain image classification. Expert Syst Appl 2011;38 (8):10049–53.
• [13] Saritha M, Joseph KP, Mathew AT. Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recogn Lett 2013;34(16):2151–6.
• [14] Kalbkhani H, Shayesteh MG, Zali-Vargahan B. Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series. Biomed Signal Process Control 2013;8(6):909–19.
• [15] Zacharaki EI, Wang S, Chawla S, Soo Yoo D, Wolf R, Melhem ER, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 2009;62(6):1609–18.
• [16] Paul JS, Plassard AJ, Landman BA, Fabbri D. Deep learning for brain tumor classification. Proc of SPIE. 2016. pp. 1013710–1.
• [17] Khawaldeh S, Pervaiz U, Rafiq A, Alkhawaldeh RS. Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks. Appl Sci 2017;8(1):27.
• [18] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436–44.
• [19] Mohan G, Subashini MM. MRI based medical image analysis: survey on brain tumor grade classification. Biomed Signal Process Control 2018;39:139–61.
• [20] 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.
• [21] Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, et al. Brain tumor segmentation with deep neural networks. Med Image Anal 2017;35:18–31.
• [22] Kleesiek J, Urban G, Hubert A, Schwarz D, Maier-Hein K, Bendszus M, et al. Deep MRI brain extraction: a 3D convolutional neural network for skull stripping. NeuroImage 2016;129:460–9.
• [23] IXI Dataset. Available from: http://brain-development.org/ixi-dataset/.
• [24] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 2013;26(6):1045–57.
• [25] Scarpace L, Flanders AE, Jain R, Mikkelsen T, Andrews DW. Data from REMBRANDT. Cancer Imaging Archive 2015.
• [26] Scarpace L, Mikkelsen T, Cha S, Rao S, Tekchandani S, Gutman D, et al. Radiology data from the cancer genome atlas glioblastoma multiforme [TCGA-GBM] collection. Cancer Imaging Archive 2016.
• [27] Pedano N, Flanders AE, Scarpace L, Mikkelsen T, Eschbacher JM, Hermes B, et al. Radiology data from the cancer genome atlas low grade glioma [TCGA-LGG] collection. Cancer Imaging Archive 2016.
• [28] Cheng J, Huang W, Cao S, Yang R, Yang W, Yun Z, et al. Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE 2015;10(10). e0140381.
• [29] Goodfellow I, Bengio Y, Courville A. Deep learning. MIT Press; 2016.
• [30] LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series. Handb Brain Theory Neural Netw 1995;3361(10):1995.
• [31] He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision; 2015. p. 1026–34.
• [32] Clevert D-A, Unterthiner T, Hochreiter S. Fast and accurate deep network learning by exponential linear units (elus); 2015, arXiv preprint arXiv:1511.07289.
• [33] Klambauer G, Unterthiner T, Mayr A, Hochreiter S. Self-normalizing neural networks; 2017, arXiv preprint arXiv:1706.02515.
• [34] Kingma D, Ba J. Adam: a method for stochastic optimization; 2014, arXiv preprint arXiv:1412.6980.
• [35] Duchi J, Hazan E, Singer Y. Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 2011;12(July):2121–59.
• [36] Zeiler MD. ADADELTA: an adaptive learning rate method; 2012, arXiv preprint arXiv:1212.5701.
• [37] Sutskever I, Martens J, Dahl G, Hinton G. On the importance of initialization and momentum in deep learning. International Conference on Machine Learning. 2013. pp. 1139–47.
• [38] Deepa SN. Introduction to genetic algorithms. Berlin Heidelberg: Springer-Verlag; 2008.
• [39] Dietterich TG. Ensemble methods in machine learning. Multiple Classifier Syst 2000;1857:1–15.
• [40] Pan Y, Huang W, Lin Z, Zhu W, Zhou J, Wong J, et al. Brain tumor grading based on neural networks and convolutional neural networks. Engineering in Medicine and Biology Society (EMBC), 37th Annual International Conference of the IEEE. 2015. pp. 699–702.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).