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The impact of applying barrel distortion correction to endoscopic imagery in the context of automated celiac disease diagnosis is experimentally investigated. For a large set of feature extraction techniques, it is found that contrasting to intuition, no improvement but even significant result degradation of classification accuracy can be observed. For techniques relying on geometrical properties...
In this paper, scale invariant features are extracted from different variants of the Dual-Tree Complex Wavelet Transform (DT-CWT) in order to classify high-magnification colon endoscopy imagery with respect to the pit pattern scheme. To enhance the scale invariance, the Discrete Cosine Transform is applied to the feature vectors, that are achieved from a DT-CWT variant. The feature vectors either...
This paper describes an application of machine learning techniques and evolutionary algorithms to colon cancer diagnosis. We propose an automated classification system for endoscopical images, which is supposed to support physicians in making correct decisions. Classification is done according to the pit-pattern scheme, which defines two/six different classes based on the occurrence of patterns on...
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