A deep learning based approach to classification of CT brain images

Gao, Xiaohong W. and Hui, Rui (2016) A deep learning based approach to classification of CT brain images. In: SAI Computing Conference 2016, 13-15 Jul 2016, London, UK.

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Abstract

This study explores the applicability of the state of the art of deep learning convolutional neural network (CNN) to the classification of CT brain images, aiming at bring images into clinical applications. Towards this end, three categories are clustered, which contains subjects’ data with either Alzheimer’s disease (AD) or lesion (e.g. tumour) or normal ageing. Specifically, due to the characteristics of CT brain images with larger thickness along depth (z) direction (~5mm), both 2D and 3D CNN are employed in this research. The fusion is therefore conducted based on both 2D CT images along axial direction and 3D segmented blocks with the accuracy rates are 88.8%, 76.7% and 95% for classes of AD, lesion and normal respectively, leading to an average of 86.8%.

Item Type: Conference or Workshop Item (Speech)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 18918
Notes on copyright: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Xiaohong Gao
Date Deposited: 25 Feb 2016 12:12
Last Modified: 02 Apr 2019 15:09
URI: https://eprints.mdx.ac.uk/id/eprint/18918

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