Title
Lung disease stratification using amalgamation of Riesz and Gabor transforms in machine learning framework.
Abstract
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.
Year
DOI
Venue
2017
10.1016/j.compbiomed.2017.08.014
Computers in Biology and Medicine
Keywords
Field
DocType
Lung cancer,Computer tomography,CADx cascaded system,Riesz transforms,Gabor transforms,Amalgamation,Risk stratification,Performance
Lung cancer,Feature selection,Computer science,Lung disease,Radiation treatment planning,Robustness (computer science),Artificial intelligence,High-resolution computed tomography,Grayscale,Riesz transform,Computer vision,Pattern recognition,Machine learning
Journal
Volume
Issue
ISSN
89
C
0010-4825
Citations 
PageRank 
References 
2
0.50
13
Authors
9
Name
Order
Citations
PageRank
Joel C. Than1262.52
Luca Saba220224.44
Norliza Mohd. Noor3379.25
M. Omar4427.77
Rosminah M. Kassim5262.52
Ashari Yunus6303.68
Harman S Suri721.85
Michele Porcu820.84
Jasjit S. Suri91754128.89