Barrett's Esophagus Identification Using Optimum-Path Forest

Computer-assisted analysis of endoscopic imagescan be helpful to the automatic diagnosis and classificationof neoplastic lesions. Barrett's esophagus (BE) is a commontype of reflux that is not straightforward to be detected byendoscopic surveillance, thus being way susceptible to erroneousdiagn...

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Bibliographic Details
Main Authors: Souza, Luis A., Afonso, Luis C. S., Palm, Christoph, Papa, Joao P.
Format: Conference Proceeding
Language:English
Subjects:
Barrett's esophagus
Biopsy
Cancer
Esophagus
Feature extraction
machine learning
pattern recognition
Support vector machines
Training
Vegetation
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Summary:Computer-assisted analysis of endoscopic imagescan be helpful to the automatic diagnosis and classificationof neoplastic lesions. Barrett's esophagus (BE) is a commontype of reflux that is not straightforward to be detected byendoscopic surveillance, thus being way susceptible to erroneousdiagnosis, which can cause cancer when not treated properly. In this work, we introduce the Optimum-Path Forest (OPF) classifier to the task of automatic identification of Barrett'sesophagus, with promising results and outperforming the wellknownSupport Vector Machines (SVM) in the aforementionedcontext. We consider describing endoscopic images by means offeature extractors based on key point information, such as theSpeeded up Robust Features (SURF) and Scale-Invariant FeatureTransform (SIFT), for further designing a bag-of-visual-wordsthat is used to feed both OPF and SVM classifiers. The bestresults were obtained by means of the OPF classifier for bothfeature extractors, with values lying on 0.732 (SURF) - 0.735(SIFT) for sensitivity, 0.782 (SURF) - 0.806 (SIFT) for specificity, and 0.738 (SURF) - 0.732 (SIFT) for the accuracy.
ISSN:2377-5416
DOI:10.1109/SIBGRAPI.2017.47