Hybrid feature selection-based feature fusion for liver disease classification on ultrasound images

2020 ◽  
pp. 145-164
Author(s):  
Puja Bharti ◽  
Deepti Mittal
Keyword(s):  
Feature Selection ◽  
Liver Disease ◽  
Feature Fusion ◽  
Disease Classification ◽  
Ultrasound Images

Staging of Fatty Liver Diseases Based on Hierarchical Classification and Feature Fusion for Back-Scan–Converted Ultrasound Images

2016 ◽  
Vol 39 (2) ◽  
pp. 79-95 ◽  
Author(s):  
Mehri Owjimehr ◽  
Habibollah Danyali ◽  
Mohammad Sadegh Helfroush ◽  
Alireza Shakibafard
Keyword(s):  
Liver Disease ◽  
Fatty Liver ◽  
Fatty Liver Disease ◽  
Feature Fusion ◽  
Wavelet Packet ◽  
Hierarchical Classification ◽  
Support Vector ◽  
Ultrasound Images ◽  
Early Stages ◽  
Occurrence Matrix

Fatty liver disease is progressive and may not cause any symptoms at early stages. This disease is potentially fatal and can cause liver cancer in severe stages. Therefore, diagnosing and staging fatty liver disease in early stages is necessary. In this paper, a novel method is presented to classify normal and fatty liver, as well as discriminate three stages of fatty liver in ultrasound images. This study is performed with 129 subjects including 28 normal, 47 steatosis, 42 fibrosis, and 12 cirrhosis images. The proposed approach uses back-scan conversion of ultrasound sector images and is based on a hierarchical classification. The proposed algorithm is performed in two parts. The first part selects the optimum regions of interest from the focal zone of the back-scan–converted ultrasound images. In the second part, discrimination between normal and fatty liver is performed and then steatosis, fibrosis, and cirrhosis are classified in a hierarchical basis. The wavelet packet transform and gray-level co-occurrence matrix are used to obtain a number of statistical features. A support vector machine classifier is used to discriminate between normal and fatty liver, and stage fatty cases. The results of the proposed scheme clearly illustrate the efficiency of this system with overall accuracy of 94.91% and also specificity of more than 90%.

scholarly journals Machine Learning-Based Identification of Potentially Novel Non-Alcoholic Fatty Liver Disease Biomarkers

Biomedicines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1636
Author(s):  
Roshan Shafiha ◽  
Basak Bahcivanci ◽  
Georgios V. Gkoutos ◽  
Animesh Acharjee
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Liver Disease ◽  
Fatty Liver Disease ◽  
Complement Pathway ◽  
Alcoholic Fatty Liver ◽  
Potential Biomarker ◽  
Processing Step ◽  
Biological Interpretation ◽  
Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease that presents a great challenge for treatment and prevention.. This study aims to implement a machine learning approach that employs such datasets to identify potential biomarker targets. We developed a pipeline to identify potential biomarkers for NAFLD that includes five major processes, namely, a pre-processing step, a feature selection and a generation of a random forest model and, finally, a downstream feature analysis and a provision of a potential biological interpretation. The pre-processing step includes data normalising and variable extraction accompanied by appropriate annotations. A feature selection based on a differential gene expression analysis is then conducted to identify significant features and then employ them to generate a random forest model whose performance is assessed based on a receiver operating characteristic curve. Next, the features are subjected to a downstream analysis, such as univariate analysis, a pathway enrichment analysis, a network analysis and a generation of correlation plots, boxplots and heatmaps. Once the results are obtained, the biological interpretation and the literature validation is conducted over the identified features and results. We applied this pipeline to transcriptomics and lipidomic datasets and concluded that the C4BPA gene could play a role in the development of NAFLD. The activation of the complement pathway, due to the downregulation of the C4BPA gene, leads to an increase in triglyceride content, which might further render the lipid metabolism. This approach identified the C4BPA gene, an inhibitor of the complement pathway, as a potential biomarker for the development of NAFLD.

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