Automated optimized classification techniques for magnetic resonance brain images
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Automated optimized classification techniques for magnetic resonance brain images Ahmed S. Elkorany 1,2 & Zeinab F. Elsharkawy 3 Received: 24 May 2019 / Revised: 29 June 2020 / Accepted: 9 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
This paper presents automatic tumor detection and classification approaches for brain magnetic resonance images (MRI). These approaches are based on hybrid-optimized classification techniques and classify brain MRI to healthy, benign or malignant. The proposed system implements three-optimization techniques combined with Artificial Neural Network (ANN). Multi-Verse Optimizer (MVO), Moth-Flame Optimizer (MFO) and Salp Swarm Algorithm (SSA) are used and compared to examine how these techniques could be successfully employed to enhance the classification accuracy via selecting the optimal parameters of ANN. The proposed techniques are applied to the Harvard database and BRATS challenge dataset to evaluate the performance via Receiver Operation Characteristics (ROC) analysis. The approaches are tested against geometric transformations such as scaling, rotation and warping to show how much the proposed system resists these transformations. Experimentally, the proposed algorithms achieve the highest classification accuracy as compared to the other published ones. Also, the MVOANN algorithm outperforms the other proposed algorithms. Keywords Image processing . Pattern recognition . Artificial neural networks . Multi-verse optimizer . Moth-flame optimizer . Salp swarm algorithm
* Zeinab F. Elsharkawy [email protected]
1
Department of Electronics and Electrical Comm. Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
2
High Institute of Electronic Engineering, Ministry of Higher Education and Scientific Research, Belbeis, Elsharkia, Egypt
3
Engineering Department, Nuclear Research Center, Atomic Energy Authority, Cairo, Egypt
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1 Introduction Brain diseases like brain tumors raised very fast. It is one of the major causes of cancer-related death among adults and children in the world. In the brain tumor, the abnormal cells grow inside or around the brain. Several imaging techniques are used for brain tumor detection such as X-Ray, Portion Emission Tomography (PET), Computed Tomography (CT) and MRI [1]. MRI is widely used among all imaging techniques to visualize brain soft tissues. Compared with other techniques, MRI results in high contrast and high-quality images with detailed information that is needed for brain tumor detection, plus it is a nondestructive imaging technique [2]. The early detection of brain tumors allows the prompt treatment that increases the possibility of healing. Recent work shows that the brain tumor detection can be done via supervised classification techniques like k-nearest neighbors (k-NN) [3], Support Vector Machine (SVM) [4] and ANN [3], Probabilistic Neural Network (PNN) [5], Convolution Neural Network (CNN)[6], Deep Neural Networ
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