An enhanced deep learning architecture for classification of Tuberculosis types from CT lung images

Gao, Xiaohong W. ORCID logoORCID:, Comley, Richard A. ORCID logoORCID: and Khan, Maleika Heenaye-Mamode (2020) An enhanced deep learning architecture for classification of Tuberculosis types from CT lung images. 2020 IEEE International Conference on Image Processing (ICIP). In: ICIP 2020: 27th IEEE International Conference on Image Processing, 25-28 Oct 2020, Abu Dhabi, Unites Arab Emirates (Virtual Conference). e-ISBN 9781728163956, pbk-ISBN 9781728163963. ISSN 1522-4880 [Conference or Workshop Item] (doi:10.1109/ICIP40778.2020.9190815)

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In this work, an enhanced ResNet deep learning network, depth-ResNet, has been developed to classify the five types of Tuberculosis (TB) lung CT images. Depth-ResNet takes 3D CT images as a whole and processes the volumatic blocks along depth directions. It builds on the ResNet-50 model to obtain 2D features on each frame and injects depth information at each process block. As a result, the averaged accuracy for classification is 71.60% for depth-ResNet and 68.59% for ResNet. The datasets are collected from the ImageCLEF 2018 competition with 1008 training data in total, where the top reported accuracy was 42.27%.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 30256
Notes on copyright: © 2020 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: 26 May 2020 15:57
Last Modified: 29 Nov 2022 18:12

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