Abstract
Accurate diagnosis of brain diseases is a challenging task for the physicians. The manual detection of brain disorders is infeasible and tedious task. Magnetic resonance imaging (MRI) of brain images is one of the advanced neuroimaging techniques used in recent days. The research reveals that the machine learning techniques have significant contributions for the classification of brain MRI images. Extreme learning machine (ELM) has been used in image classification. In this paper, an attempt has been made to hybridize the convolutional neural network (CNN) model. The proposed CNN-ELM model integrates the CNN model for feature engineering and the ELM method for classification of brain MRI images. For simulation study, brain tumor MRI image dataset is considered and the results suggest that the proposed CNN-ELM model outperformed the CNN model in classifying the brain images in the dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kim, Y.K., Na, K.S.: Application of machine learning classification for structural brain MRI in mood disorders: critical review from a clinical perspective. Prog. Neuropsychopharmacol. Biol. Psychiatry 80, 71–80 (2018)
Grover, V.P., Tognarelli, J.M., Crossey, M.M., Cox, I.J., Taylor-Robinson, S.D., McPhail, M.J.: Magnetic resonance imaging: principles and techniques: lessons for clinicians. J. Clin. Exp. Hepatology 5(3), 246–255 (2015)
Mohan, G., Subashini, M.M.: MRI based medical image analysis: survey on brain tumor grade classification. Biomed. Signal Process. Control 39, 139–161 (2018)
Bernal, J., Kushibar, K., Asfaw, D.S., Valverde, S., Oliver, A., Martı, R., Lladó, X.: Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif. Intell. Med. 95, 64–81 (2019)
Talo, M., Yildirim, O., Baloglu, U.B., Aydin, G., Acharya, U.R.: Convolutional neural networks for multi-class brain disease detection using MRI images. Computerized Med. Imaging Graph. 78 (2019)
Jain, R., Jain, N., Aggarwal, A., Hemanth, D.J.: Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cogn. Syst. Res. 57, 147–159 (2019)
Cinar, A., Yildırım, M.: Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Med. Hypotheses 109684 (2020)
Zhou, S., Tan, B.: Electrocardiogram soft computing using hybrid deep learning CNN-ELM. Appl. Soft Comput. 86, 105778 (2020)
Duan, M., Li, K., Yang, C., Li, K.: A hybrid deep learning CNN–ELM for age and gender classification. Neurocomputing 275, 448–461 (2018)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst., Man, Cybern. Part B (Cybernetics) 42(2), 513–529 (2011)
Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybernet. 2(2), 107–122 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Satapathy, P., Pradhan, S.K., Hota, S., Mahakud, R.R. (2021). Brain Image Classification Using the Hybrid CNN Architecture. In: Das, S., Mohanty, M.N. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 202. Springer, Singapore. https://doi.org/10.1007/978-981-16-0695-3_32
Download citation
DOI: https://doi.org/10.1007/978-981-16-0695-3_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-0694-6
Online ISBN: 978-981-16-0695-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)