Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE

• Autorzy: Hashemzehi Raheleh , Mahdavi Seyyed Javad Seyyed , Kheirabadi Maryam , Kamel Seyed Reza

Identyfikatory

DOI

10.1016/j.bbe.2020.06.001

• Języki publikacji: EN

Abstrakty

EN

A brain tumor is an abnormal growth of cells inside the skull. Malignant brain tumors are among the most dreadful types of cancer with direct consequences such as cognitive decline and poor quality of life. Analyzing magnetic resonance imaging (MRI) is a popular technique for brain tumor detection. In this paper, we use these images to train our new hybrid paradigm which consists of a neural autoregressive distribution estimation (NADE) and a convolutional neural network (CNN). We subsequently test this model with 3064 T1-weight-ed contrast-enhanced images with three types of brain tumors. The results demonstrate that the hybrid CNN-NADE has a high classification performance as regards the availability of medical images are limited.

Słowa kluczowe

EN

convolutional neural network; neural autoregressive distribution estimation; brain tumor; magnetic resonance imaging

PL

sieć neuronowa konwolucyjna; guz mózgu; rezonans magnetyczny

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 3
• Strony: 1225--1232
• Opis fizyczny: Bibliogr. 43 poz., rys., tab.

Twórcy

autor

Hashemzehi Raheleh

• Department of Computer Science, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran

autor

Mahdavi Seyyed Javad Seyyed

• Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

autor

Kheirabadi Maryam

• Department of Computer Science, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran

autor

Kamel Seyed Reza

• Department of Software Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

Bibliografia

• [1] Development of deep learning algorithm using convolutional neural network for medical imaging. Int J Eng Adv Technol 2020;9(3):139–42.
• [2] Ari A, Hanbay D. Deep learning based brain tumor classification and detection system. Turkish J Electr Eng Comput Sci 2018;26(5):2275–86.
• [3] Swati ZNK, et al. Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph 2019;75:34–46.
• [4] Sharif M, Amin J, Raza M, Yasmin M, Satapathy SC. An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor. Pattern Recognit Lett 2020;129:150–7.
• [5] Kabir Anaraki A, Ayati M, Kazemi F. Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybern Biomed Eng 2019;39(1):63–74.
• [6] Talo M, Baloglu UB, Yildirim Ö, Rajendra Acharya U. Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn Syst Res 2019;54:176–88.
• [7] Thaha MM, Kumar KPM, Murugan BS, Dhanasekeran S, Vijayakarthick P, Selvi AS. Brain tumor segmentation using convolutional neural networks in MRI images. J Med Syst 2019;43(9):294.
• [8] Tandel GS, et al. A review on a deep learning perspective in brain cancer classification. Cancers 2019;11(1).
• [9] Gumaei A, Hassan MM, Hassan MR, Alelaiwi A, Fortino G. A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access 2019;7:36266–73.
• [10] El-Dahshan EAS, Mohsen HM, Revett K, Salem ABM. Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst Appl 2014;41(11):5526–45.
• [11] Oliva JB, et al. Transformation autoregressive networks. 35th International Conference on machine learning ICML 2018, 9. 2018;p. 6243–58.
• [12] Van Den Oord A, Kalchbrenner N, Vinyals O, Espeholt L, Graves A, Kavukcuoglu K. Conditional image generation with PixelCNN decoders. Advances in neural information processing systems. 2016;4797–805.
• [13] Kanmani P, Marikkannu P. MRI brain images classification: a multi-level threshold based region optimization technique. J Med Syst 2018;42(4).
• [14] Tiwari A, Srivastava S, Pant M. Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognit Lett 2020;131:244–60.
• [15] Alqazzaz S, Sun X, Yang X, Nokes L. Automated brain tumor segmentation on multi-modal MR image using SegNet. Comput Vis Media 2019;5(2):209–19.
• [16] Kamnitsas K, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 2017;36:61–78.
• [17] Sugimori H, Kawakami M. Automatic detection of a standard line for brain magnetic resonance imaging using deep learning. Appl Sci (Basel) 2019;9(18):3849.
• [18] Ismail M, et al. A fast stochastic framework for automatic MR brain images segmentation. PLoS One 2017;12(11): e0187391.
• [19] Siva Raja PM, Rani AV. Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach. Biocybern Biomed Eng 2020;40(1):440–53.
• [20] Gumaste PP, Bairagi DVK. A hybrid method for brain tumor detection using advanced textural feature extraction. Biomed Pharmacol J 2020;13(1).
• [21] El-Dahshan ESA, Hosny T, Salem ABM. Hybrid intelligent techniques for MRI brain images classification. Digit Signal Process A Rev J 2010;20(2):433–41.
• [22] Hong KS, Khan MJ, Hong MJ. Feature extraction and classification methods for hybrid fNIRS-EEG brain-computer interfaces. Front Hum Neurosci 2018;12. Frontiers Media S.A..
• [23] Kaldera HNTK, Gunasekara SR, DIssanayake MB. Brain tumor classification and segmentation using faster R-CNN. 2019 advances in science and engineering technology international conferences, ASET 2019; 2019.
• [24] Bernal J, et al. Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif Intell Med 2019;95:64–81.
• [25] Seetha J, Raja SS. Brain tumor classification using convolutional neural networks. Biomed Pharmacol J 2018;11(3):1457–61.
• [26] Wang K, Savva M, Chang AX, Ritchie D. Deep convolutional priors for indoor scene synthesis. ACM Trans Graph 2018;37(4).
• [27] Zhang Y, Xiang T, Hospedales TM, Lu H. Deep mutual learning. Proceedings of the IEEE computer society conference on computer vision and pattern recognition; 2018;4320–8.
• [28] Kushibar K, et al. Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features. Med Image Anal 2018;48:177–86.
• [29] Uria B, Cote MA, Gregor K, Murray I, Larochelle H. Neural autoregressive distribution estimation. J Mach Learn Res 2016;17:1–37.
• [30] Khoshaman A, Vinci W, Denis B, Andriyash E, Sadeghi H, Amin MH. Quantum variational autoencoder. Quantum Sci Technol 2018;4(1):014001.
• [31] Uria B, Murray I, Larochelle H. RNADE : the real-valued neural autoregressive. Conf Neural Inf Process Syst 2013;1–9.
• [32] Zhang S, Yao L, Sun A, Tay Y. ‘‘Deep learning based recommender system: a survey and new perspectives,’’ ACM Computing Surveys, 52, 1. Association for Computing Machinery; 2019.
• [33] Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 1980;36(4):193–202.
• [34] Hubel DH, Wiesel TN. Receptive fields and functional architecture of monkey striate cortex. J Physiol (Paris) 1968;195(1):215–43.
• [35] Lecun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436–44. Nature Publishing Group.
• [36] LeCun Y, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput 1989;1(4):541–51.
• [37] Özyurt F, Sert E, Avci E, Dogantekin E. Brain tumor detection based on Convolutional Neural Network with neutrosophic expert maximum fuzzy sure entropy. Meas J Int Meas Confed 2019;147.
• [38] Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging 2018;9(4):611–29.
• [39] Germain M, Gregor K, Murray I, Larochelle H. MADE: masked autoencoder for distribution estimation. In 32nd International Conference on machine learning ICML 2015, 2. 2015;p. 881–9.
• [40] Cheng J, et al. Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS One 2015;10(10).
• [41] Cheng J, et al. Retrieval of brain tumors by adaptive spatial pooling and Fisher vector representation. PLoS One 2016;11(6).
• [42] Carneiro T, Da Nobrega RVM, Nepomuceno T, Bin Bian G, De Albuquerque VHC, Filho PPR. Performance analysis of google colaboratory as a tool for accelerating deep learning applications. IEEE Access 2018;6:61677–85.
• [43] Roy P, Ghosh S, Bhattacharya S, Pal U. Effects of degradations on deep neural network architectures; 2018.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).