8 February 2017 Detection of tuberculosis using hybrid features from chest radiographs
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Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 102252B (2017) https://doi.org/10.1117/12.2266795
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan
Abstract
Tuberculosis is an infectious disease and becomes a major threat all over the world but still diagnosis of tuberculosis is a challenging task. In literature, chest radiographs are considered as most commonly used medical images in under developed countries for the diagnosis of TB. Different methods have been proposed but they are not helpful for radiologists due to cost and accuracy issues. Our paper presents a methodology in which different combinations of features are extracted based on intensities, shape and texture of chest radiograph and given to classifier for the detection of TB. The performance of our methodology is evaluated using publically available standard dataset Montgomery Country (MC) which contains 138 CXRs among which 80 CXRs are normal and 58 CXRs are abnormal including effusion and miliary patterns etc. The accuracy of 81.16% was achieved and the results show that proposed method have outperformed existing state of the art methods on MC dataset.
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Ayesha Fatima, M. Usman Akram, Mahmood Akhtar, Irrum Shafique, "Detection of tuberculosis using hybrid features from chest radiographs", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102252B (8 February 2017); doi: 10.1117/12.2266795; https://doi.org/10.1117/12.2266795
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