Radiomic-based framework for early diagnosis of lung cancer

Document Type

Conference Proceeding

Source of Publication

Proceedings - International Symposium on Biomedical Imaging

Publication Date

4-1-2019

Abstract

© 2019 IEEE. This paper proposes a new framework for pulmonary nodule diagnosis using radiomic features extracted from a single computed tomography (CT) scan. The proposed framework integrates appearance and shape features to get a precise diagnosis for the extracted lung nodules. The appearance features are modeled using 3D Histogram of Oriented Gradient (HOG) and higher-order Markov Gibbs random field (MGRF) model because of their ability to describe the spatial non-uniformity in the texture of the nodule regardless of its size. The shape features are modeled using Spherical Harmonic expansion and some basic geometric features in order to have a full description of the shape complexity of the nodules. Finally, all the modeled features are fused and fed to a stacked autoencoder to differentiate between the malignant and benign nodules. Our framework is evaluated using 727 nodules which are selected from the Lung Image Database Consortium (LIDC) dataset, and achieved classification accuracy, sensitivity, and specificity of 93.12%, 92.47%, and 93.60% respectively.

ISBN

9781538636411

ISSN

1945-7928

Publisher

IEEE Computer Society

Volume

2019-April

First Page

1293

Last Page

1297

Disciplines

Computer Sciences

Keywords

Autoencoder, Computer aided diagnosis, Computer tomography, HOG, Mgrf, Spherical harmonics

Scopus ID

85073908410

Indexed in Scopus

yes

Open Access

no

Link to Full Text

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