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Lung disease stratification using amalgamation of Riesz and Gabor transforms in machine learning framework

Than, J. C. M. and Saba, L. and Noor, N. M. and Rijal, O. M. and Kassim, R. M. and Yunus, A. and Suri, H. S. and Porcu, M. and Suri, J. S. (2017) Lung disease stratification using amalgamation of Riesz and Gabor transforms in machine learning framework. Computers in Biology and Medicine, 89 . pp. 197-211. ISSN 0010-4825

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Lung disease risk stratification is important for both diagnosis and treatment planning, particularly in biopsies and radiation therapy. Manual lung disease risk stratification is challenging because of: (a) large lung data sizes, (b) inter- and intra-observer variability of the lung delineation and (c) lack of feature amalgamation during machine learning paradigm. This paper presents a two stage CADx cascaded system consisting of: (a) semi-automated lung delineation subsystem (LDS) for lung region extraction in CT slices followed by (b) morphology-based lung tissue characterization, thereby addressing the above shortcomings. LDS primarily uses entropy-based region extraction while ML-based lung characterization is mainly based on an amalgamation of directional transforms such as Riesz and Gabor along with texture-based features comprising of 100 greyscale features using the K-fold cross-validation protocol (K = 2, 3, 5 and 10). The lung database consisted of 96 patients: 15 normal and 81 diseased. We use five high resolution Computed Tomography (HRCT) levels representing different anatomy landmarks where disease is commonly seen. We demonstrate the amalgamated ML stratification accuracy of 99.53%, an increase of 2% against the conventional non-amalgamation ML system that uses alone Riesz-based feature embedded with feature selection based on feature strength. The robustness of the system was determined based on the reliability and stability that showed a reliability index of 0.99 and the deviation in risk stratification accuracies less than 5%. Our CADx system shows 10% better performance when compared against the mean of five other prominent studies available in the current literature covering over one decade.

Item Type:Article
Uncontrolled Keywords:Lung disease, radiation therapy
Subjects:T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:75954
Deposited By: Widya Wahid
Deposited On:30 May 2018 04:27
Last Modified:30 May 2018 04:27

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