Article: Segmentation and classification of brain tumour with optimisation enabled deep learning using MRI images Journal: International Journal of Bio-Inspired Computation (IJBIC) 2025 Vol.26 No.1 pp.35 - 50 Abstract: The detection of brain tumour at final stage is difficult to heal and the diagnosis of brain tumour from large image database is difficult. Due to the various sizes, shapes, and locations of tumours in the brain, the present techniques are insufficient to give precise classification. Hence, an effective model for classifying and segmenting brain tumours is developed in this study using the circle inspired teaching learning optimisation method (CITLO). During the first step, an input MRI image from a dataset is obtained, and the obtained image is supplied into the pre-processing module. Following that, SegNet, which is trained using CITLO, is employed for tumour segmentation. The brain tumour classification process employs deep convolutional neural network (DCNN), with classifier hyper parameters learned using CITLO. The CITLO_DCNN attained a maximum accuracy of 95.8%, a sensitivity of 96.9%, a specificity of 96.6%, a maximum segmentation accuracy of 95.7%, and ROC of 93.1%. Inderscience Publishers - linking academia, business and industry through research

Title: Segmentation and classification of brain tumour with optimisation enabled deep learning using MRI images

Authors: Sajeev Ram Arumugam; Sakthi Ulaganathan; Rajeshkannan Regunathan; S. Vimala

Addresses: Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, India ' Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, 603203, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Vellore-632014, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Prathyusha Engineering College, Thiruvallur, Tamil Nadu, India

Abstract: The detection of brain tumour at final stage is difficult to heal and the diagnosis of brain tumour from large image database is difficult. Due to the various sizes, shapes, and locations of tumours in the brain, the present techniques are insufficient to give precise classification. Hence, an effective model for classifying and segmenting brain tumours is developed in this study using the circle inspired teaching learning optimisation method (CITLO). During the first step, an input MRI image from a dataset is obtained, and the obtained image is supplied into the pre-processing module. Following that, SegNet, which is trained using CITLO, is employed for tumour segmentation. The brain tumour classification process employs deep convolutional neural network (DCNN), with classifier hyper parameters learned using CITLO. The CITLO_DCNN attained a maximum accuracy of 95.8%, a sensitivity of 96.9%, a specificity of 96.6%, a maximum segmentation accuracy of 95.7%, and ROC of 93.1%.

Keywords: SegNet; MRI image; circle inspired teacher learning optimisation; deep learning; tumour segmentation; deep convolutional neural network; DCNN.

DOI: 10.1504/IJBIC.2025.148389

International Journal of Bio-Inspired Computation, 2025 Vol.26 No.1, pp.35 - 50

Received: 07 Feb 2023
Accepted: 27 May 2024
Published online: 03 Sep 2025 *

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Title: Segmentation and classification of brain tumour with optimisation enabled deep learning using MRI images

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