{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:39:02Z","timestamp":1777891142055,"version":"3.51.4"},"reference-count":34,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["THC"],"published-print":{"date-parts":[[2023,5,12]]},"abstract":"<jats:p>BACKGROUND: Fatty liver disease is a common condition caused by excess fat in the liver. It consists of two types: Alcoholic Fatty Liver Disease, also called alcoholic steatohepatitis, and Non-Alcoholic Fatty Liver Disease (NAFLD). As per epidemiological studies, fatty liver encompasses 9% to 32% of the general population in India and affects overweight people. OBJECTIVE: An Optimized Support Vector Machine with Support Vector Regression model is proposed to evaluate the volume of liver fat by image analysis (LFA-OSVM-SVR). METHOD: The input computed tomography (CT) liver images are collected from the Chennai liver foundation and Liver Segmentation (LiTS) datasets. Here, input datasets are pre-processed using Gaussian smoothing filter and bypass filter to reduce noise and improve image intensity. The proposed U-Net method is used to perform the liver segmentation. The Optimized Support Vector Machine is used to classify the liver images as fatty liver image and normal images. The support vector regression (SVR) is utilized for analyzing the fat in percentage. RESULTS: The LFA-OSVM-SVR model effectively analyzed the liver fat from CT scan images. The proposed approach is activated in python and its efficiency is analyzed under certain performance metrics. CONCLUSION: The proposed LFA-OSVM-SVR method attains 33.4%, 28.3%, 25.7% improved accuracy with 55%, 47.7%, 32.6% lower error rate for fatty image classification and 30%, 21%, 19.5% improved accuracy with 57.9%, 46.5%, 31.76% lower error rate for normal image classificationthan compared to existing methods such as Convolutional Neural Network (CNN) with Fractional Differential Enhancement (FDE) (CNN-FDE), Fully Convolutional Networks (FCN) and Non-negative Matrix Factorization (NMF) (FCN-NMF), and Deep Learning with Fully Convolutional Networks (FCN) (DL-FCN).<\/jats:p>","DOI":"10.3233\/thc-220254","type":"journal-article","created":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T14:40:28Z","timestamp":1672756828000},"page":"867-886","source":"Crossref","is-referenced-by-count":11,"title":["Liver fat analysis using optimized support vector machine with support vector regression"],"prefix":"10.1177","volume":"31","author":[{"given":"B.","family":"Pushpa","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Annamalai University, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B.","family":"Baskaran","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, Annamalai University, Chidambaram, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Vivekanandan","sequence":"additional","affiliation":[{"name":"Managing Director and Liver Transplant Surgeon, Department of HPB and Liver Transplantation, RPS Hospitals, Chennai, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P.","family":"Gokul","sequence":"additional","affiliation":[{"name":"Department of Biotechnology, Saveetha School of Engineering, Chennai, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/THC-220254_ref1","doi-asserted-by":"crossref","first-page":"154355","DOI":"10.1016\/j.metabol.2020.154355","article-title":"Use of imaging techniques for non-invasive assessment in the diagnosis and staging of non-alcoholic fatty liver disease","volume":"112","author":"Miele","year":"2020","journal-title":"Metabolism."},{"issue":"3","key":"10.3233\/THC-220254_ref2","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.jhep.2018.10.033","article-title":"Non-alcoholic fatty liver disease \u2013 a global public health perspective","volume":"70","author":"Younossi","year":"2019","journal-title":"Journal of Hepatology."},{"issue":"2","key":"10.3233\/THC-220254_ref3","doi-asserted-by":"crossref","first-page":"101797","DOI":"10.1016\/j.clinre.2021.101797","article-title":"Liver fibrosis staging by computed tomography: Prospective randomized multicentric evaluation of image analyses","volume":"46","author":"Vitellius","year":"2022","journal-title":"Clinics and Research in Hepatology and Gastroenterology."},{"issue":"2","key":"10.3233\/THC-220254_ref4","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.jiac.2021.10.013","article-title":"Fatty liver on computed tomography scan on admission is a risk factor for severe coronavirus disease","volume":"28","author":"Okuhama","year":"2022","journal-title":"Journal of Infection and Chemotherapy."},{"issue":"3","key":"10.3233\/THC-220254_ref5","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.ultrasmedbio.2020.10.025","article-title":"Use of a convolutional neural network and quantitative ultrasound for diagnosis of fatty liver","volume":"47","author":"Nguyen","year":"2021","journal-title":"Ultrasound in Medicine & Biology."},{"key":"10.3233\/THC-220254_ref6","doi-asserted-by":"crossref","first-page":"154259","DOI":"10.1016\/j.metabol.2020.154259","article-title":"Advances in non-invasive biomarkers for the diagnosis and monitoring of non-alcoholic fatty liver disease","volume":"111","author":"Long","year":"2020","journal-title":"Metabolism."},{"key":"10.3233\/THC-220254_ref7","doi-asserted-by":"crossref","unstructured":"Shajin FH, Rajesh P, Raja MR. 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