Automatic Classification Algorithm for Diffused Liver Diseases based on Ultrasound Images

Diffuse liver diseases such as fatty liver and cirrhosis, are leading causes of disability and fatality across the world. Early diagnosis of these diseases is extremely important to save lives and improve the effectiveness of treatment. This study proposes a non-invasive method for diagnosing liver...

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Bibliographic Details
Published in:IEEE access 2021-01, Vol.9, p.1-1
Main Authors: Gaber, Ahmed, Hamdy, A., Abdelaal, Hammam M., Elkattan, Ahmed, Elshourbagy, Motasem M., Youness, Hassan A.
Format: Article
Language:English
Subjects:
Algorithms
Classification
Classifiers
Fats
Feature extraction
Feature selection
Fisher discriminant
Germanium
Homogeneity
Image classification
Liver
Liver cirrhosis
Liver diseases
Magnetic resonance imaging
Majority based Classifier
Medical imaging
Medical services
Recognition
Region of Interest
Ultrasonic imaging
Ultrasound
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Summary:Diffuse liver diseases such as fatty liver and cirrhosis, are leading causes of disability and fatality across the world. Early diagnosis of these diseases is extremely important to save lives and improve the effectiveness of treatment. This study proposes a non-invasive method for diagnosing liver diseases using ultrasound images, by classifying liver tissue as normal, steatosis, or cirrhosis, using feature extraction, feature selection, and classification. First, the correlation, homogeneity, variance, entropy, contrast, energy, long run emphasis, run percentage, and standard deviation are determined. Second, the most efficient features are selected based on the Fisher discriminant and manual selection methods. Third, three voting-based sub-classifiers are used, namely, the normal/steatosis, normal/cirrhosis, and steatosis/cirrhosis classifiers. The final liver tissue classification is based on the majority function. Our classification method provides two key contributions: combination of two different feature selection methods, avoiding the limitations of each method while benefiting from their strengths; and classifier categorization into three sub-classifiers, where the overall classification is based on the decision of each individual sub-classifier. We obtained recognition accuracies for the normal/steatosis, normal/cirrhosis, and steatosis/cirrhosis classifiers as 95%, 95.74%, and 94.23%, respectively, and an overall recognition accuracy of 95%, which outperforms other methods.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3049341