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1 October 2004 Medical image classification using genetic-algorithm based fuzzy-logic approach
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In this paper we present a genetic-algorithm-based fuzzy-logic approach for computer-aided diagnosis scheme in medical imaging. The scheme is applied to discriminate myocardial heart disease from echocardiographic images and to detect and classify clustered microcalcifications from mammograms. Unlike the conventional types of membership functions such as trapezoid, triangle, S curve, and singleton used in fuzzy reasoning, Gaussian-distributed fuzzy membership functions (GDMFs) are employed in the present study. The GDMFs are initially generated using various texture-based features obtained from reference images. Subsequently the shapes of GDMFs are optimized by a genetic-algorithm learning process. After optimization, the classifier is used for disease discrimination. The results of our experiments are very promising. We achieve an average accuracy of 96% for myocardial heart disease and accuracy of 88.5% at 100% sensitivity level for microcalcification on mammograms. The results demonstrated that our proposed genetic-algorithm-based fuzzy-logic approach is an effective method for computer-aided diagnosis in disease classification.
©(2004) Society of Photo-Optical Instrumentation Engineers (SPIE)
Du-Yih Tsai, Yongbum Lee, Masaru Sekiya, and Masaki Ohkubo "Medical image classification using genetic-algorithm based fuzzy-logic approach," Journal of Electronic Imaging 13(4), (1 October 2004).
Published: 1 October 2004

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