Diagnosis of lung nodule using reinforcement learning and geometric measures

Authors:
Aristófanes Correâ Silva;Valdeci Ribeiro da Silva;Areolino de Almeida Neto;Anselmo Cardoso de Paiva
Affiliations:
Department of Electrical Engineering, Federal University of Maranhão – UFMA, São Luís, MA, Brazil;Department of Computer Science, Federal University of Maranhão – UFMA, São Luís, MA, Brazil;Department of Electrical Engineering, Federal University of Maranhão – UFMA, São Luís, MA, Brazil;Department of Computer Science, Federal University of Maranhão – UFMA, São Luís, MA, Brazil
Venue:
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2005

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Abstract

This paper uses a set of 3D geometric measures with the purpose of characterizing lung nodules as malignant or benign. Based on a sample of 36 nodules, 29 benign and 7 malignant, these measures are analyzed with a technique for classification and analysis called reforcement learning. We have concluded that this techinique allows good discrimination from benign to malignant nodules.