Article: 3D segmentation of brain tumour Journal: International Journal of Engineering Systems Modelling and Simulation (IJESMS) 2024 Vol.15 No.2 pp.76 - 83 Abstract: Brain replacement is a grouping of abnormal cells in the brain. Brain tumours may be malignant or non-cancerous. Glioma, meningioma, and pituitary are common brain tumours. MRI scans may detect these cancers at various stages. There are many ways to classify and extract features from MRI brain tumour pictures. The CNN image classification approach accurately detects early-stage tumours. We first explore the 3D CNN using brain tumour segmentation. Then, we train several CNN architectures to compare their performance and design, in order to collect local and contextual data. One of the drawbacks of 3D design is memory use. 3D convolutions are computationally intensive and have exponential parameters. However, if correctly done, automatic identification of crucial traits without human supervision is conceivable. Its tremendous computational efficiency makes it the most often used design. Our main objective is to optimise memory consumption and processing to detect brain cancers in 3D MRI data. The 3D CNN architecture removes brain tumours first, then feeds them to a pre-trained feature extraction for CNN model. Using these extracted features, the best features are chosen by correlation-based output. This is done through feed-forward neural networks. Inderscience Publishers - linking academia, business and industry through research

Title: 3D segmentation of brain tumour

Authors: Rudresh Deepak Shirwaikar; Kruthika Ramesh; Abu Mohammed Faisal; M. Jeshwanth; Aditya Raghav

Addresses: Department of ISE, Ramaiah Institute of Technology, Bangalore, Karnataka, India ' Department of ISE, BMS Institute of Technology and Management, Bangalore, Karnataka, India ' Department of ISE, BMS Institute of Technology and Management, Bangalore, Karnataka, India ' Department of ISE, BMS Institute of Technology and Management, Bangalore, Karnataka, India ' Department of ISE, BMS Institute of Technology and Management, Bangalore, Karnataka, India

Abstract: Brain replacement is a grouping of abnormal cells in the brain. Brain tumours may be malignant or non-cancerous. Glioma, meningioma, and pituitary are common brain tumours. MRI scans may detect these cancers at various stages. There are many ways to classify and extract features from MRI brain tumour pictures. The CNN image classification approach accurately detects early-stage tumours. We first explore the 3D CNN using brain tumour segmentation. Then, we train several CNN architectures to compare their performance and design, in order to collect local and contextual data. One of the drawbacks of 3D design is memory use. 3D convolutions are computationally intensive and have exponential parameters. However, if correctly done, automatic identification of crucial traits without human supervision is conceivable. Its tremendous computational efficiency makes it the most often used design. Our main objective is to optimise memory consumption and processing to detect brain cancers in 3D MRI data. The 3D CNN architecture removes brain tumours first, then feeds them to a pre-trained feature extraction for CNN model. Using these extracted features, the best features are chosen by correlation-based output. This is done through feed-forward neural networks.

Keywords: tumour; CNN; segmentation; U-net architecture; accuracy; encoder; decoder; training; validation; dice score.

DOI: 10.1504/IJESMS.2024.136973

International Journal of Engineering Systems Modelling and Simulation, 2024 Vol.15 No.2, pp.76 - 83

Received: 14 Sep 2021
Accepted: 28 Jan 2022
Published online: 01 Mar 2024 *

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Title: 3D segmentation of brain tumour

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