{"doi":"10.1109/ius46767.2020.9251611","title":"Support vector machine (SVM) based liver classification: fibrosis, steatosis, and inflammation","abstract":"An SVM based liver classifier was developed to differentiate liver conditions, including normal, fibrosis with low fat, fibrosis with high fat, and inflammation. An in-vivo study was performed with 35 rats under normal conditions or after carbon tetrachloride (CCl4) or concanavalin A (ConA) dosing to induce fibrosis with varying degrees of steatosis, and inflammation, respectively. These livers were imaged in-vivo by an ultrasound, and approximately 30 frames for each rat were acquired. Therefore, a total of 998 ultrasound images were analyzed and used for training a SVM classifier. Each image has three measured parameters: H-scan scattering classification, estimated attenuation coefficient, and B-scan intensity. These parameters were assigned as inputs to the SVM. A liver diagnosis system based on the SVM and H-scan was produced. The clusters representing each state of liver are provided in two- and three- parameter space. From these, the SVM generates decision planes to classify the liver conditions. The classification accuracy is 92.2% with the three features. Therefore, these results provide the beginning of a coherent framework for determining the scattering signatures or clustering in multi-parametric space, of the normal liver compared with diseased livers.","journal":null,"year":2020,"id":119944,"datarank":0.7366031617867792,"base_score":2.70805020110221,"endowment":2.70805020110221,"self_citation_contribution":0.40620753016533157,"citation_network_contribution":0.3303956316214476,"self_endowment_contribution":0.40620753016533157,"citer_contribution":0.3303956316214476,"corpus_percentile":null,"corpus_rank":null,"citation_count":14,"citer_count":4,"citers_with_citation_signal":4,"citers_with_endowment":4,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9639,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":472626,"name":"Terri A. Swanson","orcid":null,"position":1,"is_corresponding":false},{"id":472116,"name":"Theresa Tuthill","orcid":"0000-0003-0170-3974","position":2,"is_corresponding":false},{"id":404054,"name":"Kevin J. Parker","orcid":"0000-0002-6313-6605","position":3,"is_corresponding":false},{"id":404053,"name":"Jihye Baek","orcid":"0000-0002-7140-4390","position":0,"is_corresponding":true}],"reference_count":24,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:14:13.002105Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}