{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T17:11:30Z","timestamp":1790097090657,"version":"4.0.1"},"reference-count":30,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Ultrason Imaging"],"abstract":"<jats:p>Ovarian cyst identification is a critical task in gynecological diagnostics, aiming to distinguish between benign and malignant cysts for timely and appropriate treatment. Conventional models often use hand-engineered features, but this approach fails to capture the rich variability and detailed complexity found in ultrasound and MRI images. Additionally, classical approaches are prone to lower accuracy and poor generalizability across diverse patient populations and imaging conditions. Thus, this paper introduces a Quantum-inspired Levenberg\u2013Marquardt Network (Quantum-LMNet) for ovarian cyst detection. Initially, the preprocessing is done using the Contra-Harmonic Mean Filter. The segmentation of cyst regions is carried out with AuxSegNet, employing multiple loss functions including multi-label soft margin loss, Dice loss, and focal loss. Features are subsequently derived from the Discrete Curvelet Transform, Local Quantized Patterns (LQP), and statistical metrics. Finally, ovarian cysts are identified using the proposed Quantum-LMNet, which combines the Levenberg\u2013Marquardt Network (LMNet), with Quantum-Inspired Convolutional Neural Networks (QuCNet). The Quantum-LMNet also showed notable enhancement across several evaluation metrics, including Accuracy, True Positive Rate (TPR), True Negative Rate (TNR), False Omission Rate (FOR), Matthews Correlation Coefficient (MCC), and Cohen\u2019s Kappa, achieving values of 96.157%, 95.720%, 97.007%, 0.099, 0.970, and 95.359%, respectively.<\/jats:p>","DOI":"10.1177\/01617346261478286","type":"journal-article","created":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T08:33:36Z","timestamp":1789806816000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantum-inspired Levenberg\u2013Marquardt Network for Ovarian Cyst Identification Using Ultrasound Image"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8120-2098","authenticated-orcid":false,"given":"C.A.","family":"Daphine Desona Clemency","sequence":"first","affiliation":[{"name":"Department of CSE, Sathyabama Institute of Science and Technology, Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshila Grace","family":"L K","sequence":"additional","affiliation":[{"name":"Department of CSE, Sathyabama Institute of Science and Technology, Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,19]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2012.02.002"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2006.877092"},{"key":"e_1_3_2_4_2","first-page":"24","article-title":"Ultrasound imaging and image segmentation in the area of ultrasound: a review","author":"Saini K","year":"2010","unstructured":"Saini K, Dewal ML, Rohit M. 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An intelligent machine learning approach for ovarian detection and classification system using ultrasonogram images. Eng Sci. 2023;23:879.","journal-title":"Eng Sci"},{"issue":"9","key":"e_1_3_2_14_2","first-page":"799","article-title":"Identifying and classifying an ovarian cyst using SCBOD (size and count-based ovarian detection) algorithm in ultrasound image","volume":"13","author":"Jeevitha S","year":"2022","unstructured":"Jeevitha S, Priya N. Identifying and classifying an ovarian cyst using SCBOD (size and count-based ovarian detection) algorithm in ultrasound image. 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