A Comprehensive Framework for Accurate Classification of Pulmonary Nodules

Document Type

Conference Proceeding

Source of Publication

Proceedings - International Conference on Image Processing, ICIP

Publication Date

10-1-2020

Abstract

© 2020 IEEE. A precise computerized lung nodule diagnosis framework is very important for helping radiologists to diagnose lung nodules at an early stage. In this manuscript, a novel system for pulmonary nodule diagnosis, utilizing features extracted from single computed tomography (CT) scans, is proposed. This system combines robust descriptors for both texture and contour features to give a prediction of the nodule's growth rate, which is the standard clinical information for pulmonary nodules diagnosis. Spherical Sector Isosurfaces Histogram of Oriented Gradient is developed to describe the nodule's texture, taking spatial information into account. A Multi-views Peripheral Sum Curvature Scale Space is used to demonstrate the nodule's contour complexity. Finally, the two modeled features are augmented together utilizing a deep neural network to diagnose the nodules malignancy. For the validation purpose, the proposed system utilized 727 nodules from the Lung Image Database Consortium. The proposed system classification accuracy was 94.50%.

ISBN

9781728163956

ISSN

1522-4880

Publisher

IEEE

Volume

2020-October

First Page

408

Last Page

412

Disciplines

Medicine and Health Sciences

Keywords

CAD, Computer Tomography, Curvature Scale Space, Deep Neural Network., SIHOG

Scopus ID

85098635837

Indexed in Scopus

yes

Open Access

no

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