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A novel stacked generalization of models for improved TB detection in chest radiographs.

Rajaraman, S; Candemir, S; Xue, Z; Alderson, P O; Kohli, M; Abuya, J; Thoma, G R; Antani, S.
Conf Proc IEEE Eng Med Biol Soc; 2018: 718-721, 2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-30440497
Chest x-ray (CXR) analysis is a common part of the protocol for confirming active pulmonary Tuberculosis (TB). However, many TB endemic regions are severely resource constrained in radiological services impairing timely detection and treatment. Computer-aided diagnosis (CADx) tools can supplement decision-making while simultaneously addressing the gap in expert radiological interpretation during mobile field screening. These tools use hand-engineered and/or convolutional neural networks (CNN) computed image features. CNN, a class of deep learning (DL) models, has gained research prominence in visual recognition. It has been shown that Ensemble learning has an inherent advantage of constructing non-linear decision making functions and improve visual recognition. We create a stacking of classifiers with hand-engineered and CNN features toward improving TB detection in CXRs. The results obtained are highly promising and superior to the state-of-the-art.
Selo DaSilva