Liver fibrosis processing, multiclassification, and diagnosis based on hybrid machine learning approaches - Automated method for ultrasound shear wave elastography classification

Sep 17, 2026

Uploaded by Aurelia

Liver fibrosis severity is associated with cirrhosis risk, but liver biopsy is invasive and limited by sampling error. This study proposes an automated, noninvasive workflow using ultrasound shear wave elastography images (700) and a hybrid machine learning design. Preprocessing includes contour detection and CLAHE to enhance liver tissue properties, followed by CNN-based deep feature extraction. Final fibrosis staging is performed via two classifiers, SoftMax and SVM, using a five-class scheme (normal, F1-F4), achieving 97.18% and 98.59% accuracy respectively.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Keywords

Convolutional neural network

Shear wave elastography

Liver fibrosis

Introduction

Limitations of liver biopsy

Noninvasive elastography approaches

Manual SWE variability and ROI selection

Liver fibrosis processing, multiclassification, and diagnosis based on hybrid machine learning approaches Zainab Sattar Jabbar1, Auns Qusai Al-Neami1, Ahmed A Khawwam2, Sufian Munther Salih1 1Department of Biomedical Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq 2Faculty of Dentistry, Al-Kufa University, Najaf, Iraq Article history: Received Jun 5, 2022 Revised Oct 7, 2022 Accepted Oct 11, 2022 Keywords: Convolutional neural network Deep learning Liver fibrosis Shear wave elastography Corresponding Author: The cirrhosis and cirrhosis-related problems are connected to the degree of fibrosis in the liver. The purpose of this paper is to propose an automated method for identifying liver fibrosis using ultrasound shear wave elastography (700) images that is based on a hybrid machine learning approach using a convolutional neural network (CNN) with two types of classifier (SoftMax and support vector machine (SVM)) . The dataset gathered from hospitals is used in the training and testing phases of the model. The objective is to develop a hybrid machine learning model that can classify images based on their stage of fibrosis. The suggested system comprises three stages. The first is the preprocessing step, which starts with countor detection and continues with the "contrast limited adaptive histogram equalization (CLAHE)"technique to show the properties of liver tissue. In the second step, the CNN algorithm was utilized, which was based on several images to extract deep features and identify shear wave elastography (SWE) samples. In the third step, SVM and SoftMax functions are used to classify liver fibrosis. A fiveclass model (normal, F1, F2, F3, and F4) was developed. The result illustrateshow successfully the CNN-SoftMax and CNN-SVM classifiers classified liver fibrosis in the test dataset, with 97.18% and 98.59% accuracy, respectively. This is an open access article under the CC BY-SA license. Zainab Sattar Jabbar Department of Biomedical Engineering, College of Engineering, Al-Nahrain University Al Jadriyah Bridge, Baghdad, Iraq Email: [st.zainab.s.jabbar@ced.nahrainuniv.edu.iq](st.zainab.s.jabbar@ced.nahrainuniv.edu.iq) Article Info ABSTRACT 1. INTRODUCTION Liver fibrosis develops as a result of structural or functional changes in the liver induced by viruses, immunological responses, and toxic assaults [1], [2] . The severity of liver fibrosis is linked to the risk of developing cirrhosis and cirrhosis-related complications [3] . The standard method for assessing liver fibrosis is currently a liver biopsy. However, it does have some drawbacks. It is an invasive procedure, with severe complications occurring in up to 1% of biopsy cases [4], [5] . Because biopsies only make up a small fraction of the overall liver, there is bound to be some sampling error [6] . As a result, noninvasive methods for detecting liver fibrosis, such as transient elastography, shear wave elastography, and magnetic resonance elastography, are becoming more popular [7]–[9] . The use of ultrasound shear wave elastography to identify advanced liver fibrosis has shown benefits as a non-invasive and low-cost approach. Ultrasound is used to create and measure tissue shear waves, which are then utilized to determine tissue stiffness. To determine tissue stiffness and fibrosis staging, a technician must manually select tissue for shear wave elastography (SWE) excitation using ultrasound imaging guidance, then measure tissue stiffness over a manually specified field of interest (ROI) within the stimulated tissue and also average tissue stiffness from several sources (3–10) independently acquired images. The current process's manual nature increases inter-operator variability and output quality, with blood arteries, bile ducts, and the liver capsule frequently being included in the ROI by mistake, resulting in unusually high shear wave speeds for the beneath liver parenchyma [10] . Lee et al. [

FAQs

What problem does the paper address in liver fibrosis assessment?

It targets the limitations of liver biopsy and the variability of manual ultrasound shear wave elastography (SWE) measurements when staging liver fibrosis.

How does the proposed hybrid method process the ultrasound SWE images?

It uses preprocessing with contour detection and CLAHE, then applies a CNN to extract deep features, and finally classifies fibrosis using SoftMax and SVM in a three-stage pipeline.

What fibrosis staging classes are used and what accuracy is reported?

The model is trained as a five-class system: normal, F1, F2, F3, and F4. CNN-SoftMax and CNN-SVM reach 97.18% and 98.59% accuracy on the test dataset, respectively.

Sep 17, 2026

Uploaded by Aurelia