Automatic Classification Algorithm for Diffused Liver Diseases Based on Ultrasound Images

Mabrouk, Ahmed Gaber; Hamdy, A.; Abdelaal, Hammam M.; Elkattan, Ahmed Gamal; Elshourbagy, Motasem M.; Alansary, Hassan A. Youness

doi:10.1109/access.2021.3049341

Cited by 7 publications

(1 citation statement)

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“…SVM and k‐means clustering were used to classify the normal liver and the three stages of cirrhosis. Mabrouk et al [11] proposed a noninvasive method for diagnosing liver disease using US images. First, nine features such as correlation are calculated.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Feature Fusion Method for Liver Cirrhosis Classification

Wang,

Jian,

et al. 2024

Int J Imaging Syst Tech

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Liver cirrhosis is one of the most common liver diseases in the world, posing a threat to people's daily lives. In advanced stages, cirrhosis can lead to severe symptoms and complications, making early detection and treatment crucial. This study aims to address this critical healthcare challenge by improving the accuracy of liver cirrhosis classification using ultrasound imaging, thereby assisting medical professionals in early diagnosis and intervention. This article proposes a new multiscale feature fusion network model (MSFNet), which uses the feature extraction module to capture multiscale features from ultrasound images. This approach enables the neural network to utilize richer information to accurately classify the stage of cirrhosis. In addition, a new loss function is proposed to solve the class imbalance problem in medical datasets, which makes the model pay more attention to the samples that are difficult to classify and improves the performance of the model. The effectiveness of the proposed MSFNet was evaluated using ultrasound images from 61 subjects. Experimental results demonstrate that our method achieves high classification accuracy, with 98.08% on convex array datasets and 97.60% on linear array datasets. Our proposed method can classify early, middle, and late cirrhosis very accurately. It provides valuable insights for the clinical treatment of liver cirrhosis and may be helpful for the rehabilitation of patients.

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Section: Introductionmentioning

confidence: 99%

Multiscale Feature Fusion Method for Liver Cirrhosis Classification

Wang,

Jian,

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

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Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review

et al. 2023

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Medical image analysis plays a pivotal role in the evaluation of diseases, including screening, surveillance, diagnosis, and prognosis. Liver is one of the major organs responsible for key functions of metabolism, protein and hormone synthesis, detoxification, and waste excretion. Patients with advanced liver disease and Hepatocellular Carcinoma (HCC) are often asymptomatic in the early stages; however delays in diagnosis and treatment can lead to increased rates of decompensated liver diseases, late-stage HCC, morbidity and mortality. Ultrasound (US) is commonly used imaging modality for diagnosis of chronic liver diseases that includes fibrosis, cirrhosis and portal hypertension. In this paper, we first provide an overview of various diagnostic methods for stages of liver diseases and discuss the role of Computer-Aided Diagnosis (CAD) systems in diagnosing liver diseases. Second, we review the utility of machine learning and deep learning approaches as diagnostic tools. Finally, we present the limitations of existing studies and outline future directions to further improve diagnostic accuracy, as well as reduce cost and subjectivity, while also improving workflow for the clinicians.

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