Ilango, Dhakshina and Sulthana, Razia ORCID: 0000-0001-5331-1310 (2023) Segmentation of brain tumours from MRI images using CNN. In: Inventive Systems and Control Proceedings of ICISC 2023. Lecture Notes in Networks and Systems, 672 . Springer, pp. 693-706. ISBN 9789819916238 ISSN 2367-3370 (Print), 2367-3389 (Online) (doi:https://doi.org/10.1007/978-981-99-1624-5_51)

PDF (Author's Accepted Manuscript) 42459 KAREEM_Segmentation_Of_Brain_Tumours_From_MRI_Images_Using_CNN_(AAM)_2023.pdf - Accepted Version Restricted to Repository staff only until 15 June 2024. Download (319kB) | Request a copy

Abstract

Identification of brain tumours in the early stage is key to proper treatment and diagnosis It can be classified as malignant or benign based on the aggressiveness of the tumour. To diagnose a patient, an MRI imaging device is used to obtain scans of the brain. Due to the large quantity of data produced, radiologists must perform the tedious task of going through each MRI image to identify the brain tumour's location, size, and origin. This process is prone to human error and is also time-consuming. Therefore, this paper proposes a methodology to accurately diagnose and segment the brain tumours from the MRI images using Convolutional Neural Networks (CNN) specifically U-NET architecture.

Item Type:	Conference Proceedings
Title of Proceedings:	Inventive Systems and Control Proceedings of ICISC 2023
Uncontrolled Keywords:	machine learning, biomedical image segmentation, brain tumours, convolutional neural networks
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	27 Jun 2023 09:00
URI:	http://gala.gre.ac.uk/id/eprint/42459

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