Multiclassifier for severity-level categorization of glioma tumors using multimodal magnetic resonance imaging brain images |
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Authors: | K. Michael Mahesh J. Arokia Renjit |
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Affiliation: | 1. Department of Electronics and Communication Engineering, St. Joseph College of Engineering, Chennai, Tamil Nadu, India;2. Department of Computer Science and Engineering, Jeppiaar Engineering College, Chennai, Tamil Nadu, India |
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Abstract: | Brain tumor segmentation and classification is a crucial challenge in diagnosing, planning, and treating brain tumors. This article proposes an automatic method that categorizes the severity level of the tumors to render an effective diagnosis. The proposed fractional Jaya optimizer-deep convolutional neural network undergoes the severity classification based on the features obtained from the segments of the magnetic resonance imaging (MRI) images. The segments are obtained using the particle swarm optimization that ensures the optimal selection of the segments from the MRI image and yields the core tumor and the edema tumor regions. The experimentation using the BRATS database reveals that the proposed method acquired a maximal accuracy, specificity, and sensitivity of 0.9414, 0.9429, and 0.9708, respectively. |
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Keywords: | deep convolutional neural networks Jaya optimization algorithm multimodal MRI brain images particle swarm optimization severity-level classification |
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