Diffuse lung disease classification based on texture features and weighted extreme learning machine

Diffuse lung diseases are a group of chronic disorders that affect the lungs. The highly prevalent lung patterns associated with diffuse lung diseases are emphysema, fibrosis, ground-glass opacity, and micro-nodules. For diffuse lung classification problem, TALISMAN (Texture Analysis of Lung ImageS...

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Vydáno v:Multimedia tools and applications Ročník 80; číslo 28-29; s. 35467 - 35479
Hlavní autoři: Raj, Shyla, Mahanand, B. S., Vinod, D. S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.11.2021
Springer Nature B.V
Témata:
1166: Advances of machine learning in data analytics and visual information processing
Artificial neural networks
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Datasets
Emphysema
Feature extraction
Fibrosis
Filter banks
Gabor filters
Image classification
Lung diseases
Machine learning
Multimedia Information Systems
Nodules
Special Purpose and Application-Based Systems
Texture
Intuitionistic local binary pattern
Diffuse lung diseases
Extreme learning machine
Texture features
Sample imbalance
ISSN:1380-7501, 1573-7721
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Popis
Shrnutí:Diffuse lung diseases are a group of chronic disorders that affect the lungs. The highly prevalent lung patterns associated with diffuse lung diseases are emphysema, fibrosis, ground-glass opacity, and micro-nodules. For diffuse lung classification problem, TALISMAN (Texture Analysis of Lung ImageS for Medical diagnostic AssistaNce) is one of the widely studied dataset in the literature. It is observed in the dataset that there exists sample imbalance among different tissue patterns. To address the sample imbalance in the data weighted extreme learning machine classifier is employed in this work. To overcome the intra-class and inter-class variation among the diffuse lung patterns features are extracted using the modified intuitionistic local binary pattern along with Gabor filter bank and grey level co-occurrence matrix. These combined texture features are then used to train the weighted extreme learning machine to classify the diffuse lung patterns. The performance of the proposed approach is compared with the existing works in the literature. The comparison results indicate better performance of the proposed approach for diffuse lung classification with sample imbalance.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-10469-5