{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T07:35:48Z","timestamp":1771572948910,"version":"3.50.1"},"reference-count":39,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81560294"],"award-info":[{"award-number":["81560294"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Hepatic cystic echinococcosis (HCE) still has a high misdiagnosis rate, and misdiagnosis may lead to wrong treatments seriously harmful for the patients. Precise diagnosis of HCE relies heavily on the experience of clinical experts with auxiliary diagnostic tools using medical images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This paper intends to improve the diagnostic accuracy for HCE by employing a method which combines deep learning with ensemble method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We proposed a method, namely HCEs\u2010Net, for classification of five HCE subtypes using ultrasound images. It takes first the snap\u2010shot strategy to obtain sub\u2010models from the pre\u2010trained VGG19, ResNet18, ViT\u2010Base, and ConvNeXt\u2010T models, then a stacking process to ensemble those sub\u2010models. Afterwards, it uses the tree\u2010structured Pazren estimator (TPE) to optimize the hyperparameters. The experiments were evaluated by the five\u2010fold cross\u2010validation process.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 3083 abdominal ultrasound images from 972 patients covering five subtypes of HCE were utilized in this study. The experiments were conducted to predict the HCE subtype, and results of modeling performance evaluation were reported in terms of precision, recall, F1\u2010score, and AUC. The stacking model based on three ConvNeXt\u2010T sub\u2010models showed the best performance, with precision 85.9%, recall 85.5%, F1\u2010score 85.7%, and AUC 0.971 which are higher than the compared state\u2010of\u2010the\u2010art models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The stacking model of three ConvNeXt\u2010T sub\u2010models shows comparable or superior performance to the other methods, including VGG19, ResNet18 and ViT\u2010Base. It has the potential to enhance clinical diagnosis for HCE.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16444","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T07:20:04Z","timestamp":1684135204000},"page":"4244-4254","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["HCEs\u2010Net: Hepatic cystic echinococcosis classification ensemble model based on tree\u2010structured Parzen estimator and snap\u2010shot approach"],"prefix":"10.1002","volume":"50","author":[{"given":"Zhengye","family":"Wang","sequence":"first","affiliation":[{"name":"College of Public Health Xinjiang Medical University  Urumqi China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kadiliya","family":"Kuerban","sequence":"additional","affiliation":[{"name":"College of Public Health Xinjiang Medical University  Urumqi 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