{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:17:43Z","timestamp":1759331863461,"version":"3.41.2"},"reference-count":31,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T00:00:00Z","timestamp":1711756800000},"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":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Non\u2010cancerous growths called uterine fibroids develop in the uterus. They can vary in size, location, and number, and can produce symptoms including excessive menstrual flow, pelvic discomfort, and reproductive problems. Early detection of uterine fibroids is important because it allows for timely intervention and appropriate management strategies. Extracting meaningful features from ultrasound (US) images requires robust and effective techniques. However, different feature extraction methods may yield varying results, and the choice of technique can influence the accuracy of fibroid detection. This paper presents an efficient approach for early detection of uterine fibroids in US images. Our proposed technique combines efficient feature extraction methods and a hybrid deep learning approach. Firstly, we employ the well\u2010known canny edge detection algorithm to accurately identify the edges of the fibroid region in the US images. This step helps in segmenting the target edge portion for further analysis. Additionally, we introduce an improved bird swarm optimization (IBSO) algorithm to extract a comprehensive set of features, utilizing both known features and newly obtained features. This approach enhances the accuracy of fibroid detection in uterine images. The proposed method uses a convolutional recurrent neural network (CRNN) to accurately detect and treat uterine fibroids, ensuring timely diagnosis and treatment. Our proposed IBSO\u2010CRNN model produces accuracy rates of <jats:bold>99.897%<\/jats:bold> and <jats:bold>97.568%<\/jats:bold> on the augmented and original datasets, respectively.<\/jats:p>","DOI":"10.1002\/ima.23073","type":"journal-article","created":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T10:50:32Z","timestamp":1711795832000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Efficient feature extraction and hybrid deep learning for early identification of uterine fibroids in ultrasound images"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1901-8764","authenticated-orcid":false,"given":"Meena","family":"Lekshmanan Chinna","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering R.M.D. Engineering College  Kavaraipettai India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4708-8308","authenticated-orcid":false,"given":"Joe 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