Abstract
In this paper we study the problem of classification of wireless capsule endoscopy images (WCE). We aim at developing a computer system that would aid in medical diagnosis by automatically detecting images containing pathological alterations in an 8-hour-long WCE video. We focus on three classes of pathologies – ulcers, bleedings and petechia – since they are typical for several diseases of the intestines. The main contribution is the performance evaluation of five feature selection and classification algorithms: minimization of classification error probability, Vector Supported Convex Hull, Support Vector Machines, Radial Basis Function and Perceptron-based Neural Networks, in application to WCE images. Experimental results show that none of the methods ouperforms the others in all tested pathology classes. Instead, a classifier ensemble can be built to accumulate evidence from multiple learning schemes, each specialized in recognition of a single type of abnormality.
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Szczypiński, P., Klepaczko, A., Strzelecki, M. (2011). An Intelligent Automated Recognition System of Abnormal Structures in WCE Images. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21219-2_19
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DOI: https://doi.org/10.1007/978-3-642-21219-2_19
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