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Article type: Research Article
Authors: Perumal, T. Sudarson Ramaa | Jegatheesan, A.b | Jayachandran, A.c; *
Affiliations: [a] Department of CSE, Rohini College of Engineering and Technology, Nagercoil, India | [b] Institute of CSE, Saveetha School of Engineering, Saveetha School of Medical and Technical Sciences, Chennai, India | [c] Department of CSE, Presidency University, Bangalore, India
Correspondence: [*] Corresponding author. A. Jayachandran, Professor, Department of CSE, Presidency University, Bangalore. E-mail: ajaya1675@gmail.com, Orchid ID: 0000-0002-8185-3237
Abstract: Brain tumor is one of the deadliest cancerous diseases and their severity has turned them into the leading cause of cancer-related mortality. Automatic detection and classification of severity-level for a brain tumor using MRI is a complex process in multilevel classification and needs an improved learning method without computational complexity. In this research article, we propose an innovative Multi-Dimensional Cascades Neural Network work (MDCNet) that takes full advantage of two networks with different dimensions, which can balance the complete semantic information and high-resolution detail information of a large-volume MRI image. In stage 1, a shallow-layer-enhanced 3D location net obtains the location and rough segmentation of brain lesions. In stage 2, a high-resolution attention map is used to obtain the 2D high-resolution image slice sets from the original image and the output of stage 1. The high-resolution images pick up the lost detailed information, refining the boundaries further. Moreover, a multi-view 2.5D net composed of three 2D refinement sub-networks is applied to deeply explore the morphological characteristics of all brain lesions from different perspectives, which compensates for the mistakes and missing spatial information of a single view, increasing the stability of the whole algorithm. The robustness of the proposed model is analyzed using several performance metrics of three different data sets. Through the prominent performance, the proposed model can outperform other existing models attaining an average accuracy of 99.13%. Here, the individual accuracy for Dataset 1, Dataset 2, and Dataset 3 is 99.67%, 98.16%, and 99.76% respectively.
Keywords: Keywords Brain tumor, classification, convolutional neural network, two stage ensemble, magnetic resonance imaging
DOI: 10.3233/JIFS-220308
Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4717-4732, 2023
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