{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:19:23Z","timestamp":1778347163956,"version":"3.51.4"},"reference-count":54,"publisher":"Wiley","license":[{"start":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T00:00:00Z","timestamp":1676332800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2023,2,14]]},"abstract":"<jats:p>One of the leading causes of female infertility is PCOS, which is a hormonal disorder affecting women of childbearing age. The common symptoms of PCOS include increased acne, irregular period, increase in body hair, and overweight. Early diagnosis of PCOS is essential to manage the symptoms and reduce the associated health risks. Nonetheless, the diagnosis is based on Rotterdam criteria, including a high level of androgen hormones, ovulation failure, and polycystic ovaries on the ultrasound image (PCOM). At present, doctors and radiologists manually perform PCOM detection using ovary ultrasound by counting the number of follicles and determining their volume in the ovaries, which is one of the challenging PCOS diagnostic criteria. Moreover, such physicians require more tests and checks for biochemical\/clinical signs in addition to the patient\u2019s symptoms in order to decide the PCOS diagnosis. Furthermore, clinicians do not utilize a single diagnostic test or specific method to examine patients. This paper introduces the data set that includes the ultrasound image of the ovary with clinical data related to the patient that has been classified as PCOS and non-PCOS. Next, we proposed a deep learning model that can diagnose the PCOM based on the ultrasound image, which achieved 84.81% accuracy using the Inception model. Then, we proposed a fusion model that includes the ultrasound image with clinical data to diagnose the patient if they have PCOS or not. The best model that has been developed achieved 82.46% accuracy by extracting the image features using MobileNet architecture and combine with clinical features.<\/jats:p>","DOI":"10.1155\/2023\/9686697","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T18:55:02Z","timestamp":1676400902000},"page":"1-15","source":"Crossref","is-referenced-by-count":73,"title":["A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS)"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3637-500X","authenticated-orcid":true,"given":"Abrar","family":"Alamoudi","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1002-6178","authenticated-orcid":true,"given":"Irfan Ullah","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1619-5733","authenticated-orcid":true,"given":"Nida","family":"Aslam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1166-0541","authenticated-orcid":true,"given":"Nourah","family":"Alqahtani","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1376-7652","authenticated-orcid":true,"given":"Hind S.","family":"Alsaif","sequence":"additional","affiliation":[{"name":"Department of Radiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2472-8261","authenticated-orcid":true,"given":"Omran","family":"Al Dandan","sequence":"additional","affiliation":[{"name":"Department of Radiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6443-9336","authenticated-orcid":true,"given":"Mohammed","family":"Al Gadeeb","sequence":"additional","affiliation":[{"name":"Department of Radiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8040-1223","authenticated-orcid":true,"given":"Ridha","family":"Al Bahrani","sequence":"additional","affiliation":[{"name":"Department of Radiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.fertnstert.2015.08.002"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1148\/rg.326125503"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-51281-5"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-07762-3"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/S1472-6483(10)61644-6"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1210\/mend.13.6.0311"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1093\/humrep\/deh098"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2018.11.020"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.3390\/s22124570"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.3390\/bdcc6040107"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1109\/TENCON.2019.8929674"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1002\/uog.23530"},{"issue":"1","key":"13","first-page":"43","article-title":"A hybrid model of PSO algorithm and artificial neural network for automatic follicle classification","volume":"21","author":"O. R. Isah","year":"2017","journal-title":"Int. J. Bioautomation"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1109\/IES50839.2020.9231695"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1109\/ICoICT.2015.7231453"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-020-0109-6"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105361"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksus.2020.04.005"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.08.015"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/971\/1\/012005"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1109\/ICoICT.2017.8074702"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02199-1"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1109\/ICoICT.2015.7231458"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.9756\/bijsesc.9017"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105621"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1109\/TENSYMP50017.2020.9230932"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1109\/CCWC51732.2021.9375994"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1186\/s40738-019-0067-7"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2946092"},{"key":"30","first-page":"295","article-title":"Deep learning approaches for gynaecological ultrasound image segmentation: a radio-frequency vs. B-mode comparison","author":"C. Carvalho"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC44109.2020.9175358"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103545"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1109\/iccvw54120.2021.00368"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2018.8512468"},{"key":"35","article-title":"Transfer learning techniques","volume-title":"Big Data Technologies And Applications","author":"K. W. M. K. Wang","year":"2016"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.1109\/cvprw.2009.5206848"},{"key":"37","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"K. Simonyan"},{"issue":"2","key":"38","article-title":"Deep learning frameworks for computer aided diagnosis based on medical images","volume":"82","author":"Q. Zhang","year":"2021","journal-title":"Dissertation abstracts international. B, The sciences and engineering"},{"key":"39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"40","first-page":"2818","article-title":"Rethinking the inception architecture for computer vision","author":"C. Szegedy"},{"key":"41","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106311"},{"key":"42","article-title":"Mobilenets: efficient convolutional neural networks for mobile vision applications","author":"A. G. Howard","year":"2017"},{"key":"43","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1524\/1\/012105"},{"key":"44","doi-asserted-by":"crossref","article-title":"Densely connected convolutional networks","author":"G. Huang","DOI":"10.1109\/CVPR.2017.243"},{"key":"45","doi-asserted-by":"publisher","DOI":"10.1186\/s13634-021-00755-1"},{"key":"46","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture11070617"},{"key":"47","article-title":"Data fusion lexicon","volume":"15","author":"E. F. White","year":"1991","journal-title":"Technical Panel C3"},{"key":"48","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/704504"},{"key":"49","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00341-z"},{"key":"50","article-title":"Multimodal classification for analysing social media","author":"C. T. Duong","year":"2017"},{"key":"51","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-7078-0_3"},{"key":"52","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"53","first-page":"1","article-title":"Adam: a method for stochastic optimization","author":"D. P. Kingma"},{"key":"54","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/971\/1\/012016"}],"container-title":["Applied Computational Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2023\/9686697.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2023\/9686697.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2023\/9686697.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T18:55:09Z","timestamp":1676400909000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/acisc\/2023\/9686697\/"}},"subtitle":[],"editor":[{"given":"Ridha","family":"Ejbali","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2023,2,14]]},"references-count":54,"alternative-id":["9686697","9686697"],"URL":"https:\/\/doi.org\/10.1155\/2023\/9686697","relation":{},"ISSN":["1687-9732","1687-9724"],"issn-type":[{"value":"1687-9732","type":"electronic"},{"value":"1687-9724","type":"print"}],"subject":[],"published":{"date-parts":[[2023,2,14]]}}}