On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model

Ashwini Pradhan, Debahuti Mishra, Kaberi Das, Ganapati Panda, Sachin Kumar and Mikhail Zymbler
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Ashwini Pradhan: Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan Deemed to Be University, Bhubaneswar 751030, Odisha, India
Debahuti Mishra: Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan Deemed to Be University, Bhubaneswar 751030, Odisha, India
Kaberi Das: Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan Deemed to Be University, Bhubaneswar 751030, Odisha, India
Ganapati Panda: Department of Electronics and Tele Communication, C. V. Raman Global University, Bhubaneswar 752054, Odisha, India
Sachin Kumar: College of Information Business Systems, National University of Science and Technology, MISIS, Leninsky Prospect 4, 119049 Moscow, Russia
Mikhail Zymbler: Department of Computer Science, South Ural State University, 454080 Chelyabinsk, Russia

Mathematics, 2021, vol. 9, issue 17, 1-21

Abstract: Computer-aided diagnosis permits biopsy specimen analysis by creating quantitative images of brain diseases which enable the pathologists to examine the data properly. It has been observed from other image classification algorithms that the Extreme Learning Machine (ELM) demonstrates superior performance in terms of computational efforts. In this study, to classify the brain Magnetic Resonance Images as either normal or diseased, a hybridized Salp Swarm Algorithm-based ELM (ELM-SSA) is proposed. The SSA is employed to optimize the parameters associated with ELM model, whereas the Discrete Wavelet Transformation and Principal Component Analysis have been used for the feature extraction and reduction, respectively. The performance of the proposed “ELM-SSA” is evaluated through simulation study and compared with the standard classifiers such as Back-Propagation Neural Network, Functional Link Artificial Neural Network, and Radial Basis Function Network. All experimental validations have been carried out using two different brain disease datasets: Alzheimer’s and Hemorrhage. The simulation results demonstrate that the “ELM-SSA” is potentially superior to other hybrid methods in terms of ROC, AUC, and accuracy. To achieve better performance, reduce randomness, and overfitting, each algorithm has been run multiple times and a k -fold stratified cross-validation strategy has been used.

Keywords: MRI classification; Salp Swarm Algorithm; Extreme Learning Machine; hybridized ML classifiers (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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