{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T08:51:28Z","timestamp":1791881488043,"version":"4.3.4"},"reference-count":24,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Ultrasound Medicine"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Spina bifida is a birth defect caused by the incomplete closure of the neural tube around the spinal cord. Earlier, various deep learning\u2010based models were developed, but they failed to capture the fine details from the ultrasound images that reduce the detection accuracy. Hence, to overcome these challenges, an automated and trustworthy deep learning framework is significant for accurate identification of spina bifida from second\u2010trimester fetal ultrasound images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This research proposes a novel hybrid convolutional neural network\u2010transformer model for spina bifida identification, where convolutional neural network integration enables the model to extract local features and transformer encoder integration enables the model to capture global contextual dependencies. Further, spatial feature representation is enhanced by using the multi\u2010resolution convolutional neural network blocks and positional encoding. On the other hand, clinically relevant regions are highlighted through the use of a self\u2010attention mechanism within the proposed model. The hyperparameters of the proposed model are fine\u2010tuned using a crossover boosted cheetah optimization algorithm, which integrates the strengths of the Cheetah optimizer and the Crossover Mechanism. Thereby, optimizing the performance of the proposed model and making the model robust to spina bifida identification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed model achieves robust diagnostic performance, which attains an accuracy of 99.02%, an F1\u2010score of 98.21%, and a lower inference time of 0.88\u2009second, demonstrating its suitability in real\u2010time prenatal ultrasound screening applications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed model provides an efficient and clinically viable solution for automated prenatal spina bifida detection.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jum.70347","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T21:08:47Z","timestamp":1784236127000},"page":"2533-2551","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid\n                    <scp>CNN<\/scp>\n                    \u2010Transformer Model With Crossover Boosted Cheetah Optimization for Prenatal Spina Bifida Identification From Ultrasound Images"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4350-1681","authenticated-orcid":false,"given":"Asha","family":"Rajasekaran","sequence":"first","affiliation":[{"name":"Department of CSE Sathyabama Institute of Science and Technology  Chennai India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.S. Subashka","family":"Ramesh","sequence":"additional","affiliation":[{"name":"Department of CSE SRM Institute of Science and Technology  Chennai India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.4236\/ojo.2023.1310044"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/pdi3.2517"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.29240"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/ijgo.70063"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/14767058.2025.2477770"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13023-025-03856-4"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajogmf.2024.101445"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3369155"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci13010118"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/pd.6453"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-18160-9"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105253"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3503679"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-06526-4"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108706"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2024.3485216"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-66468-x"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106791"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42600-023-00296-6"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/3605739"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-67076-5"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108110"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1049\/gtd2.13322"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-024-05889-x"}],"container-title":["Journal of Ultrasound in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jum.70347","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/jum.70347","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jum.70347","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T08:26:06Z","timestamp":1791879966000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/jum.70347"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,16]]},"references-count":24,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2026,11]]}},"alternative-id":["10.1002\/jum.70347"],"URL":"https:\/\/doi.org\/10.1002\/jum.70347","archive":["Portico"],"relation":{},"ISSN":["0278-4297","1550-9613"],"issn-type":[{"value":"0278-4297","type":"print"},{"value":"1550-9613","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,16]]},"assertion":[{"value":"2025-10-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-08","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}