Lung nodules diagnosis based on evolutionary convolutional neural network

Lung nodules diagnosis based on evolutionary convolutional neural network Lung cancer presents the highest cause of death among patients around the world, in addition of being one of the smallest survival rates after diagnosis. In this paper, we exploit a deep learning technique jointly with the genetic algorithm to classify lung nodules in whether malignant or benign, without computing the shape and texture features. The methodology was tested on computed tomography (CT) images from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best sensitivity of 94.66%, specificity of 95.14%, accuracy of 94.78% and area under the ROC curve of 0.949. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Lung nodules diagnosis based on evolutionary convolutional neural network

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4480-9
Publisher site
See Article on Publisher Site

Abstract

Lung cancer presents the highest cause of death among patients around the world, in addition of being one of the smallest survival rates after diagnosis. In this paper, we exploit a deep learning technique jointly with the genetic algorithm to classify lung nodules in whether malignant or benign, without computing the shape and texture features. The methodology was tested on computed tomography (CT) images from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best sensitivity of 94.66%, specificity of 95.14%, accuracy of 94.78% and area under the ROC curve of 0.949.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Mar 8, 2017

References

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