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

Gao, Xiaohong W. ORCID: https://orcid.org/0000-0002-8103-6624, Comley, Richard A. ORCID: https://orcid.org/0000-0003-0265-1010 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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Abstract

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%.

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