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.
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Silva, A.C., da Silva, V.R., de Almeida Neto, A., de Paiva, A.C. (2005). Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_29
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DOI: https://doi.org/10.1007/11510888_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26923-6
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