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24 March 2016 Early esophageal cancer detection using RF classifiers
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Abstract
Esophageal cancer is one of the fastest rising forms of cancer in the Western world. Using High-Definition (HD) endoscopy, gastroenterology experts can identify esophageal cancer at an early stage. Recent research shows that early cancer can be found using a state-of-the-art computer-aided detection (CADe) system based on analyzing static HD endoscopic images. Our research aims at extending this system by applying Random Forest (RF) classification, which introduces a confidence measure for detected cancer regions. To visualize this data, we propose a novel automated annotation system, employing the unique characteristics of the previous confidence measure. This approach allows reliable modeling of multi-expert knowledge and provides essential data for real-time video processing, to enable future use of the system in a clinical setting. The performance of the CADe system is evaluated on a 39-patient dataset, containing 100 images annotated by 5 expert gastroenterologists. The proposed system reaches a precision of 75% and recall of 90%, thereby improving the state-of-the-art results by 11 and 6 percentage points, respectively.
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Markus H. A. Janse, Fons van der Sommen, Svitlana Zinger, Erik J. Schoon M.D., and Peter H. N. de With "Early esophageal cancer detection using RF classifiers", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851D (24 March 2016); https://doi.org/10.1117/12.2208583
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