Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures

Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures

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 conclud...

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Hauptverfasser: Silva, Aristófanes Correâ, da Silva, Valdeci Ribeiro, de Almeida Neto, Areolino, de Paiva, Anselmo Cardoso
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Data processing. List processing. Character string processing
Exact sciences and technology
Learning and adaptive systems
Lung Nodule
Malignant Nodule
Marching Cube Algorithm
Memory organisation. Data processing
Software
Sphericity Index
Surface Type
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Zusammenfassung: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.
ISSN:0302-9743
1611-3349
DOI:10.1007/11510888_29