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Radiomic-based framework for early diagnosis of lung cancer
Authors
Ayman El-Baz
Adel Elmaghraby
+6 more
Mohammed Ghazal
Robert Keynton
Hadil Abu Khalifeh
Ahmed Shaffie
Ahmed Soliman
Fatma Taher
Publication date
1 April 2019
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Cite
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
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Last time updated on 10/08/2021
ZU Scholars (Zayed University)
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Last time updated on 03/12/2021