Lung nodules classification in CT images using shannon and simpson diversity indices and SVM

Authors:
Leonardo Barros Nascimento;Anselmo Cardoso de Paiva;Aristófanes Corrêa Silva
Affiliations:
Post-graduate Program in Engineering of Electricity, Federal University of Maranhão - UFMA, São Luis, MA, Brazil;Department of Computer Science, Federal University of Maranhao - UFMA, Sao Luís, MA, Brazil;Post-graduate Program in Engineering of Electricity, Federal University of Maranhão - UFMA, São Luis, MA, Brazil
Venue:
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2012

Citing 4
Cited 0

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Lung Nodules Classification in CT Images Using Simpson's Index, Geometrical Measures and One-Class SVM

MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Lung structure classification using 3D geometric measurements and SVM

CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Appearance analysis for diagnosing malignant lung nodules

ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, we present the use of Shannon and Simpson Diversity Indices as texture descriptors for lung nodules in Computerized Tomography (CT) images. These indices will be proposed to characterize the nodules into two classes: benign or malignant. The investigation is done using the Support Vector Machine (SVM) for classification in a dataset consisting of 73 nodules, 47 benign and 26 malignant; the results of the methodology were: sensitivity of 85.64%, specificity of 97.89% and accuracy of 92.78%.