Computer aided diagnosis of pleural effusion in tuberculosis chest radiographs

Abstract

Tuberculosis (TB) is one the leading killers in the world, and its early detection at scale is a challenge that remains. Computer Aided Detection of Tuberculosis is an important possibility for the world due to the mismatch in the incidences of this disease with the number of trained human readers for its identification. In this paper, we propose novel features for the detection of one of the symptoms observed in cases of TB, Pleural Effusion (PE). We begin by segmenting the lung regions, followed by creation of a novel feature set. We achieve an ROC of 0.961 on discriminating PE against Chest X-Rays (CXRs) without incidences of TB. To validate that our system discriminates against PE, we achieve an ROC of 0.864 against CXRs showing incidences of TB but a lack of PE. These features are then tested on two publicly available datasets (One collected from the United States, and the other from China). Due to the lack of other work for detection of PE on these datasets, a direct comparison is unfortunately not possible. However, the results obtained surpass those of work on PE detection on other private datasets.