Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture

Article

Gao, X., James-Reynolds, C. and Currie, E. 2020. Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture. Neurocomputing. 392, pp. 233-244. https://doi.org/10.1016/j.neucom.2018.12.086
TypeArticle
TitleAnalysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture
AuthorsGao, X., James-Reynolds, C. and Currie, E.
Abstract

This research investigates the application of CT pulmonary images to the detection and characterisation of TB at five levels of severity, in order to monitor the efficacy of treatment. To contend with smaller datasets (i.e. in hundreds) and the characteristics of CT TB images in which abnormalities occupy only limited regions, a 3D block-based residual deep learning network (ResNet) coupled with injection of depth information (depth-Resnet) at each layer was implemented. Progress in evaluation has been accomplished in two ways. One is to assess the proposed depth-Resnet in prediction of severity scores and another is to analyse the probability of high severity of TB. For the former, delivered results are of 92.70 ± 5.97% and 67.15 ± 1.69% for proposed depth-Resnet and ResNet-50 respectively. For the latter, two additional measures are put forward, which are calculated using (1) the overall severity (1 to 5) probability, and (2) separate probabilities of both high severity (scores of 1 to 3) and low severity (scores of 4 and 5) respectively, when scores of 1 to 5 are mapped into initial probabilities of (0.9, 0.7, 0.5, 0.3, 0.2) respectively. As a result, these measures achieve the averaged accuracies of 75.88% and 85.29% for both methods respectively.

KeywordsDeep learning; Residual deep learning network; Classification; 3D block-based image classification; Tuberculosis (TB); Severity score of TB
PublisherElsevier
JournalNeurocomputing
ISSN0925-2312
Electronic1872-8286
Publication dates
Online24 Apr 2019
Print07 Jun 2020
Publication process dates
Deposited07 Jan 2019
Accepted09 Dec 2018
Output statusPublished
Accepted author manuscript
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Copyright Statement

© 2019 Elsevier B.V. This author's accepted manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Digital Object Identifier (DOI)https://doi.org/10.1016/j.neucom.2018.12.086
Scopus EID2-s2.0-85065095376
Web of Science identifierWOS:000532703500008
LanguageEnglish
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Gao, X. and Hassan, M. 2010. Application of mesh morphing techniques in modelling 3D objects. Annual International Conference on Computer Games Multimedia and Allied Technology. Singapore 06 - 07 Apr 2010 Global Science and Technology Forum. https://doi.org/10.5176/978-981-08-5480-5_048
Learning for life: in pursuit of thinking
James-Reynolds, C., Wilson, J., Harvey, P. and Masters, J. 2006. Learning for life: in pursuit of thinking. 7th Annual Conference: Higher Education Academy: Information and Computer Sciences. Dublin, Ireland 29 - 31 Aug 2006
Stressed or stress-free mobile privacy: detecting mobile stress: ethical issues in voice analysis.
Duquenoy, P. and James-Reynolds, C. 2003. Stressed or stress-free mobile privacy: detecting mobile stress: ethical issues in voice analysis. 1st International Workshop Wireless Security Technologies 03. Middlesex University 2003
Road sign recognition by one fixation of space-variant sensor.
Gao, X., Shaposhnikov, D., Podladchikova, L., Golovan, A., Shevtsova, N. and Hong, K. 2002. Road sign recognition by one fixation of space-variant sensor. in: Gorodnich, D. and Zhang, H. (ed.) Vision Interface ’2002: proceedings Quebec Canadian Image Processing and Pattern Recognition Society.
Invariant recognition of traffic signs
Gao, X., Shaposhnikov, D., Podladchikova, L., Shevtsova, N. and Golovan, A. 2002. Invariant recognition of traffic signs.
Application of the behavioural model of vision for invariant recognition of facial and traffic sign images.
Gao, X., Shaposhnikov, D., Podladchikova, L., Golovan, A., Shevtsova, N., Gusakova, V. and Gizatdinova, Y. 2003. Application of the behavioural model of vision for invariant recognition of facial and traffic sign images. in: Gulaev, Y. and Galushkin, A. (ed.) Neurocomputers and their application. [In Russian] Moscow Radiotechnics.
Image classification based on the informative regions properties.
Gao, X., Podladchikova, L. and Shaposhnikov, D. 2003. Image classification based on the informative regions properties. in: Proceedings of PRIA-6-2002 : 6th International conference on pattern recognition and image analysis: new information technologies. MAIK Nauka/Interperiodica. pp. 439-441
Image retrieval through perceptual shape modelling.
Gao, X., Ren, M., Riley, K., Eakins, J. and Briggs, P. 2001. Image retrieval through perceptual shape modelling. London Council for Museums, Archives and Libraries.
Telemedicine in Europe.
Gao, X. 2006. Telemedicine in Europe. ChinaPacs. Beijing 14 - 16 Apr 2006
A new approach to traffic sign recognition
Gao, X., Podladchikova, L., Shaposhnikov, D., Hong, K., Batty, S., Golovan, A., Gusakova, V. and Shevtsova, N. 2002. A new approach to traffic sign recognition. in: Arabnia, H. and Mun, Y. (ed.) Proceedings of the international conference on imaging science, systems, and technology: CISST'02. Athens CSREA Press.
Road sign recognition by means of the behavioural model of vision.
Gao, X., Golovan, A., Hong, K., Podladchikova, L. and Shevtsova, N. 2002. Road sign recognition by means of the behavioural model of vision. in: Proceedings of the third all-Russian conference on neuroinformatics [In Russian]. Moscow. pp. 63-69
Colour reproduction for tele-imaging systems
Gao, X. and He, P. 2006. Colour reproduction for tele-imaging systems. Computerized medical imaging and graphics. 30 (6-7), pp. 79-84.
Measurement of vessel diameters on retinal for cardiovascular studies.
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2001. Measurement of vessel diameters on retinal for cardiovascular studies. in: Claridge, E., Bamber, J. and Marlow, K. (ed.) Medical image understanding and analysis 2001. Medical Imaging Understanding and Analysis.
Extraction of sagittal symmetry planes from PET images.
Gao, X., Batty, S., Clark, J., Fryer, T., Blandford, A. and International Association of Science and Technology for Development. 2001. Extraction of sagittal symmetry planes from PET images. in: Hamza, M. (ed.) Visualization, imaging and image processing: proceedings of the IASTED international conference. Calgary IASTED. pp. 428-433
The state of art of medical displays.
Gao, X. 2006. The state of art of medical displays. EuroPacs 2006. Trondheim, Norway 14 - 17 Jun 2006
Towards archiving wallpaper images
Gao, X., Qian, Y., Tully, T. and Hendon, Z. 2004. Towards archiving wallpaper images. in: Hamza, M. (ed.) Proceedings of the seventh IASTED international conference on computer graphics and imaging. Anaheim Acta Press. pp. 305-309
High-precision detection of facial landmarks to estimate head motions based on vision models
Gao, X., Anishenko, S., Shaposhnikov, D., Podladchikova, L., Batty, S. and Clark, J. 2007. High-precision detection of facial landmarks to estimate head motions based on vision models. Journal of computer sciences. 3 (7), pp. 528-532.
Content-based retrieval of PET images via localised anatomical texture measurements and mean activity levels
Gao, X., Batty, S., Clark, J. and Fryer, T. 2006. Content-based retrieval of PET images via localised anatomical texture measurements and mean activity levels. Computerized medical imaging and graphics. 30 (6-7), pp. 70-74.
Extraction of physiological information from 3D PET brain images.
Gao, X., Batty, S., Fryer, T., Clark, J., Turkheimer, F. and International Association of Science and Technology for Development. 2003. Extraction of physiological information from 3D PET brain images. in: Villanueva, J. (ed.) Visualization imaging and image processing. Acta Press. pp. 401-405
Extraction of features from 3D PET images.
Gao, X., Batty, S., Clark, J. and Fryer, T. 2002. Extraction of features from 3D PET images. in: Houston, A. and Zwiggelar, R. (ed.) Medical image understanding and analysis 2002. BMVA.
Towards archiving 3D PET brain images based on their physiological and visual content.
Gao, X., Batty, S., Clark, J., Fryer, T. and Turkheimer, F. 2002. Towards archiving 3D PET brain images based on their physiological and visual content. International conference on diagnostic imaging and analysis. Shanghai, China 18 - 20 Aug 2002
A new approach to estimation of non-isotropic scale factors for correction of MR distortion
Gao, X., Hui, R., White, A.S. and Tian, Z. 2009. A new approach to estimation of non-isotropic scale factors for correction of MR distortion. International Journal of Computer Assisted Radiology and Surgery. 4 (s1), pp. s349-s350.
Classification of images on the basis of the properties of informative regions.
Gao, X., Shaposhnikov, D. and Podladchikova, L. 2003. Classification of images on the basis of the properties of informative regions. Pattern Recognition and Image Analysis. 13 (2), pp. 349-352.
A fast approach to segmentation of PET brain images for extraction of features
Gao, X. and Clark, J. 2008. A fast approach to segmentation of PET brain images for extraction of features. Gao, X., Loomes, M., Comley, R., Muller, H. and Luo, S. (ed.) International Conference on Medical Imaging and Informatics (MIMI 2007). Beijing, China 14 - 16 Aug 2007 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-540-79490-5_25
Prototype system for semantic retrieval of neurological PET images
Batty, S., Clark, J., Fryer, T. and Gao, X. 2008. Prototype system for semantic retrieval of neurological PET images. Gao, X., Muller, H., Loomes, M., Comley, R. and Luo, S. (ed.) International Conference on Medical Imaging and Informatics (MIMI 2007). Beijing, China 14 - 16 Aug 2007 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-540-79490-5_23
Colour vision model-based approach for segmentation of traffic signs
Gao, X., Hong, K., Passmore, P., Podladchikova, L. and Shaposhnikov, D. 2008. Colour vision model-based approach for segmentation of traffic signs. EURASIP Journal on Image and Video Processing. 2008. https://doi.org/10.1155/2008/386705
Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach
Anishenko, S., Osimov, V., Shaposhnikov, D., Podladchikova, L., Comley, R. and Gao, X. 2008. Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach. Puuronen, S., Pechenizkiy, M., Tsymbal, A. and Lee, D. (ed.) 21st IEEE International Symposium on Computer-Based Medical Systems. Jyvaskyla, Finland 17 - 19 Jun 2008 IEEE Computer Society. pp. 50-52 https://doi.org/10.1109/CBMS.2008.19
Detection of head motions using a vision model
Gao, X., Shaposhnikov, D., Podladchikova, L., Batty, S. and Clark, J. 2007. Detection of head motions using a vision model. Bashshur, R. (ed.) 3rd IASTED International Conference on Telehealth. Montreal, QC, Canada 31 May - 01 Jun 2007 Anaheim, CA Acta Press. pp. 167-171
Recognition of traffic signs based on their colour and shape features extracted using human vision models
Gao, X., Podladchikova, L., Shaposhnikov, D., Hong, K. and Shevtsova, N. 2006. Recognition of traffic signs based on their colour and shape features extracted using human vision models. Journal of Visual Communication and Image Representation. 17 (4), pp. 675-685. https://doi.org/10.1016/j.jvcir.2005.10.003
Colour management in telemedicine
Gao, X. 2004. Colour management in telemedicine. Hamza, M. (ed.) 7th IASTED International Conference on Computer Graphics and Imaging. Kauai, Hawaii, United States 16 - 18 Aug 2004 Anaheim, CA Acta Press. pp. 361-364
Application of vision models to traffic sign recognition
Gao, X., Shaposhnikov, D. and Podladchikova, L. 2004. Application of vision models to traffic sign recognition. Kaynak, O., Alpaydin, E., Oja, E. and Xu, L. (ed.) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Istanbul, Turkey 26 - 29 Sep 2003 Berlin, Heidelberg Springer. https://doi.org/10.1007/3-540-44989-2_131
Towards content-based retrieval for wallpaper images
Qian, Y., Tully, C., Hendon, Z. and Gao, X. 2003. Towards content-based retrieval for wallpaper images. 7th IASTED International Conference on Computer Graphics and Imaging. Kauai, Hawaii, United States 16 - 18 Aug 2004 pp. 305-309
Vision models based identification of traffic signs
Gao, X., Podladchikova, L., Shaposhnikov, D., Shevtsova, N., Hong, K., Batty, S., Golovan, A. and Gusakova, V. 2002. Vision models based identification of traffic signs. 1st European Conference on Colour Graphics, Imaging, and Vision. University of Poitiers, France 02 - 05 Apr 2002 Society for Imaging Science and Technology.
Content based retrieval of lesioned brain images
Batty, S., Blandford, A., Clark, J., Fryer, T. and Gao, X. 2002. Content based retrieval of lesioned brain images. Siegel, E. and Huang, H. (ed.) SPIE Medical Imaging 2002. San Diego, California, United States 23 - 28 Feb 2002 Bellingham Society of Photo-optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.466997
A method of vessel tracking for vessel diameter measurement on retinal images
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2001. A method of vessel tracking for vessel diameter measurement on retinal images. 2001 International Conference on Image Processing. Thessaloniki, Greece 07 - 10 Oct 2001 IEEE.
Computer algorithms for the automated measurement of retinal arteriolar diameters
Chapman, N., Witt, N., Gao, X., Bharath, A., Stanton, A., Thom, S. and Hughes, A. 2001. Computer algorithms for the automated measurement of retinal arteriolar diameters. British Journal of Ophthalmology. 85 (1), pp. 74-79. https://doi.org/10.1136/bjo.85.1.74
Quantification and characterization of arteries in retinal images
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2000. Quantification and characterization of arteries in retinal images. Computer Methods and Programs in Biomedicine. 63 (2), pp. 133-146. https://doi.org/10.1016/S0169-2607(00)00082-1