{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T01:35:44Z","timestamp":1793669744159,"version":"4.5.1"},"reference-count":35,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,4,27]],"date-time":"2024-04-27T00:00:00Z","timestamp":1714176000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Clinical Ultrasound"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Uterine fibroids (UF) are the most frequent tumors in ladies and can pose an enormous threat to complications, such as miscarriage. The accuracy of prognosis may also be affected by way of doctor inexperience and fatigue, underscoring the want for automatic classification fashions that can analyze UF from a giant wide variety of images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A hybrid model has been proposed that combines the MobileNetV2 community and deep convolutional generative adversarial networks (DCGAN) into useful resources for medical practitioners in figuring out UF and evaluating its characteristics. Real\u2010time automated classification of UF can aid in diagnosing the circumstance and minimizing subjective errors. The DCGAN science is utilized for superior statistics augmentation to create first\u2010rate UF images, which are labeled into UF and non\u2010uterine\u2010fibroid (NUF) classes. The MobileNetV2 model then precisely classifies the photos based totally on this data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The overall performance of the hybrid model contrasts with different models. The hybrid model achieves a real\u2010time classification velocity of 40 frames per second (FPS), an accuracy of 97.45%, and an F1 rating of 0.9741.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>By using this deep learning hybrid approach, we address the shortcomings of the current classification methods of uterine fibroid.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcu.23703","type":"journal-article","created":{"date-parts":[[2024,4,27]],"date-time":"2024-04-27T07:09:15Z","timestamp":1714201755000},"page":"753-762","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A lightweight hybrid model for the automatic recognition of uterine fibroid ultrasound images based on deep learning"],"prefix":"10.1002","volume":"52","author":[{"given":"Peiya","family":"Cai","sequence":"first","affiliation":[{"name":"Department of Gynecology and Obstetrics Fujian Medical University 2nd Affiliated Hospital  Quanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4929-1797","authenticated-orcid":false,"given":"Tiantian","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Engineering Huaqiao University  Quanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinglai","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Physics and Information Engineering Quanzhou Normal University  Quanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peizhong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Engineering Huaqiao University  Quanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Gynecology and Obstetrics the First Hospital of Quanzhou Affiliated to Fujian Medical University  Quanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,4,27]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(00)03622-9"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","unstructured":"YangT LiP LiuP.Efficient Automatic Detection of Uterine Fibroids Based on the Scalable EfficientDet.2022 IEEE 16th International Conference on Anti\u2010counterfeiting Security and Identification(ASID) Xiamen China; 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