Machine Learning Techniques in Detecting of Pulmonary Embolisms

Computer Aided Detection (CAD) systems have recently been used by physicians to help automatically detect early forms of breast cancer in X-ray images, lung nodules in lung CT images, and polyps in colon CT images. We discuss an automatic detection mechanism using a genetic algorithms (GA) approach...

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Hauptverfasser:	Myers, M.H., Beliaev, I., King-Ip Lin
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Breast cancer Cancer detection Colonic polyps Computed tomography Lungs Machine learning Physics computing X-ray detection X-ray detectors X-ray imaging
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creator	Myers, M.H. Beliaev, I. King-Ip Lin
description	Computer Aided Detection (CAD) systems have recently been used by physicians to help automatically detect early forms of breast cancer in X-ray images, lung nodules in lung CT images, and polyps in colon CT images. We discuss an automatic detection mechanism using a genetic algorithms (GA) approach to identify and classify Pulmonary Embolisms (PE) captured through Computed Tomography Angiography (CTA). Our method enhances the performance of the classification of diseases as compared to other methodologies discussed in this paper.
doi_str_mv	10.1109/IJCNN.2007.4370987
format	Conference Proceeding
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We discuss an automatic detection mechanism using a genetic algorithms (GA) approach to identify and classify Pulmonary Embolisms (PE) captured through Computed Tomography Angiography (CTA). Our method enhances the performance of the classification of diseases as compared to other methodologies discussed in this paper.</abstract><pub>IEEE</pub><doi>10.1109/IJCNN.2007.4370987</doi><tpages>6</tpages></addata></record>
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subjects	Breast cancer Cancer detection Colonic polyps Computed tomography Lungs Machine learning Physics computing X-ray detection X-ray detectors X-ray imaging
title	Machine Learning Techniques in Detecting of Pulmonary Embolisms
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