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Automatic Tuberculosis Severity Scoring Using Machine Learning Techniques

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dc.contributor.author BELHADJ CHEIKH, Noreddine
dc.date.accessioned 2019-12-16T08:27:02Z
dc.date.available 2019-12-16T08:27:02Z
dc.date.issued 2019
dc.identifier.uri http://e-biblio.univ-mosta.dz/handle/123456789/14263
dc.description.abstract Tuberculosis disease remains a global threat and a leading cause of death. The world health organization stressed on increasing the decline rate of the disease. Early diagnosis and evaluation of the TB severity stage are important for determining the right treatment and eventually avoiding the death of curable patients cases. The voluminous amount of medical images available and the proven performance of deep learning in medical diagnosis are a motive for an automatic medical diagnosis to tackle the high demand for radiologists and reduce the costs of diagnosis. Furthermore, there has been considerable growth in recent years in the field of deep learning, which allows performing hard classification tasks. Particularly in computer vision fields, it has been tested and proven that deep convolutional neural networks (CNN) are very promising algorithms for various visual tasks. Thus, in this report, we are interested in an automatic TB severity scoring by applying deep learning techniques on annotated chest CT scans. Our contribution is based on 3 deep learners namely, Resnet50, InceptionV3Resnet, and Lungnet. Our submissions on the test corpus reached AUC value of about 65% in ImageCLEF2019 SVR sub-task. We believe that our contributions could be further improved and might give better results if applied properly and in an optimized way. en_US
dc.language.iso en en_US
dc.relation.ispartofseries MINF255;
dc.subject Tuberculosis en_US
dc.subject Computed Tomography en_US
dc.subject Image Classification en_US
dc.subject Deep learning en_US
dc.subject Severity Scoring en_US
dc.subject 3D Data Analysis en_US
dc.title Automatic Tuberculosis Severity Scoring Using Machine Learning Techniques en_US
dc.type Other en_US


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