Classification of radiographic lung pattern based on texture analysis and machine learning
Journal of Veterinary Science
; : e44-2019.
Article
в En
| WPRIM
| ID: wpr-758922
Ответственная библиотека:
WPRO
ABSTRACT
This study evaluated the feasibility of using texture analysis and machine learning to distinguish radiographic lung patterns. A total of 1200 regions of interest (ROIs) including four specific lung patterns (normal, alveolar, bronchial, and unstructured interstitial) were obtained from 512 thoracic radiographs of 252 dogs and 65 cats. Forty-four texture parameters based on eight methods of texture analysis (first-order statistics, spatial gray-level-dependence matrices, gray-level-difference statistics, gray-level run length image statistics, neighborhood gray-tone difference matrices, fractal dimension texture analysis, Fourier power spectrum, and Law's texture energy measures) were used to extract textural features from the ROIs. The texture parameters of each lung pattern were compared and used for training and testing of artificial neural networks. Classification performance was evaluated by calculating accuracy and the area under the receiver operating characteristic curve (AUC). Forty texture parameters showed significant differences between the lung patterns. The accuracy of lung pattern classification was 99.1% in the training dataset and 91.9% in the testing dataset. The AUCs were above 0.98 in the training set and above 0.92 in the testing dataset. Texture analysis and machine learning algorithms may potentially facilitate the evaluation of medical images.
Key words
Полный текст:
1
База данных:
WPRIM
Основная тема:
Pattern Recognition, Visual
/
Radiography, Thoracic
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Residence Characteristics
/
ROC Curve
/
Classification
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Neural Networks, Computer
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Fractals
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Area Under Curve
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Dataset
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Machine Learning
Тип исследования:
Prognostic_studies
Пределы темы:
Animals
Язык:
En
Журнал:
Journal of Veterinary Science
Год:
2019
Тип:
Article