{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T22:46:19Z","timestamp":1775947579008,"version":"3.50.1"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.engappai.2026.114512","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T13:37:37Z","timestamp":1773754657000},"page":"114512","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Attention-driven ConvNeXt architecture with transformer-inspired encoding for early ovarian tumor diagnosis"],"prefix":"10.1016","volume":"174","author":[{"given":"Abdulfattah","family":"Ba Alawi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0088-5825","authenticated-orcid":false,"given":"Ferhat","family":"Bozkurt","sequence":"additional","affiliation":[]},{"given":"G\u00fcl\u015fah T\u00fcm\u00fckl\u00fc","family":"\u00d6zyer","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.engappai.2026.114512_b1","doi-asserted-by":"crossref","first-page":"68","DOI":"10.26502\/fjwhd.2644-288400109","article-title":"Prediction of ovarian cancer survival using machine learning: A population-based study","volume":"6","author":"Akazawa","year":"2023","journal-title":"J. Women\u2019s Health Dev."},{"issue":"1","key":"10.1016\/j.engappai.2026.114512_b2","doi-asserted-by":"crossref","first-page":"tyaf041","DOI":"10.1093\/cybsec\/tyaf041","article-title":"On the fog\u2019s frontline: a federated machine learning approach for industrial network threat detection and intrusion prevention","volume":"11","author":"Ali","year":"2025","journal-title":"J. Cybersecur."},{"issue":"35","key":"10.1016\/j.engappai.2026.114512_b3","article-title":"Neuromorphic quantum adversarial learning (NQAL): a bio-inspired paradigm for DNS over HTTPS threat detection","volume":"2025","author":"Ali","year":"2025","journal-title":"EURASIP J. Inf. Secur."},{"key":"10.1016\/j.engappai.2026.114512_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.jnca.2025.104326","article-title":"Next-generation AI for advanced threat detection and security enhancement in DNS over HTTPS","volume":"244","author":"Ali","year":"2025","journal-title":"J. Netw. Comput. Appl."},{"key":"10.1016\/j.engappai.2026.114512_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.ibmed.2025.100227","article-title":"Hybrid vision transformer and xception model for reliable CT-based ovarian neoplasms diagnosis","volume":"11","author":"Alshdaifat","year":"2025","journal-title":"Intelligence-Based Med."},{"key":"10.1016\/j.engappai.2026.114512_b6","article-title":"LATUP-Net: A lightweight 3D attention U-Net with parallel convolutions for brain tumor segmentation","volume":"168","author":"Alwadee","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.engappai.2026.114512_b7","article-title":"AML-Net: Attention-based multi-scale lightweight model for brain tumour segmentation in Internet of Medical Things","author":"Aslam","year":"2024","journal-title":"Comput. Inf. Technol."},{"key":"10.1016\/j.engappai.2026.114512_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.108343","article-title":"Proactive and privacy-Preserving defense for DNS over HTTPS via federated AI attestation (PAFA-DoH)","volume":"196","author":"Basharat Ali","year":"2026","journal-title":"Neural Netw."},{"key":"10.1016\/j.engappai.2026.114512_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117865","article-title":"Multivariate feature selection and autoencoder embeddings of ovarian cancer clinical and genetic data","volume":"206","author":"Bote-Curiel","year":"2022","journal-title":"Expert Syst. Appl."},{"issue":"22","key":"10.1016\/j.engappai.2026.114512_b10","doi-asserted-by":"crossref","DOI":"10.1002\/cpe.7716","article-title":"Ovarian cancer detection in computed tomography images using ensembled deep optimized learning classifier","volume":"35","author":"Boyanapalli","year":"2023","journal-title":"Concurr. Comput.: Pr. Exp."},{"key":"10.1016\/j.engappai.2026.114512_b11","series-title":"A comprehensive evaluation of histopathology foundation models for ovarian cancer subtype classification","author":"Breen","year":"2024"},{"issue":"3","key":"10.1016\/j.engappai.2026.114512_b12","doi-asserted-by":"crossref","first-page":"e176","DOI":"10.1016\/S2589-7500(23)00245-5","article-title":"Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study","volume":"6","author":"Cai","year":"2024","journal-title":"Lancet Digit. Health"},{"issue":"1","key":"10.1016\/j.engappai.2026.114512_b13","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1148\/radiol.211367","article-title":"Deep learning prediction of ovarian malignancy at US compared with O-RADS and expert assessment","volume":"304","author":"Chen","year":"2022","journal-title":"Radiology"},{"key":"10.1016\/j.engappai.2026.114512_b14","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1148\/radiol.211367","article-title":"Deep learning prediction of ovarian malignancy at ultrasound compared with O-RADS and expert assessment","volume":"304","author":"Chen","year":"2022","journal-title":"Radiology"},{"key":"10.1016\/j.engappai.2026.114512_b15","article-title":"Ovarian tumor diagnosis using deep convolutional neural networks and denoising convolutional autoencoder","volume":"vol. 82","author":"Choi","year":"2022"},{"issue":"4","key":"10.1016\/j.engappai.2026.114512_b16","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1038\/nrclinonc.2013.5","article-title":"Latest research and treatment of advanced-stage epithelial ovarian cancer","volume":"10","author":"Coleman","year":"2013","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"10.1016\/j.engappai.2026.114512_b17","series-title":"ICLR","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2021"},{"key":"10.1016\/j.engappai.2026.114512_b18","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/s41746-020-00376-2","article-title":"Deep learning-enabled medical computer vision","volume":"4","author":"Esteva","year":"2021","journal-title":"NPJ Digit. Med."},{"issue":"3","key":"10.1016\/j.engappai.2026.114512_b19","doi-asserted-by":"crossref","first-page":"e179","DOI":"10.1016\/S2589-7500(21)00278-8","article-title":"Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study","volume":"4","author":"Gao","year":"2022","journal-title":"Lancet Digit. Health"},{"issue":"4","key":"10.1016\/j.engappai.2026.114512_b20","doi-asserted-by":"crossref","first-page":"643","DOI":"10.3390\/sym13040643","article-title":"Multi-modal evolutionary deep learning model for ovarian cancer diagnosis","volume":"13","author":"Ghoniem","year":"2021","journal-title":"Symmetry"},{"issue":"14","key":"10.1016\/j.engappai.2026.114512_b21","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics14141567","article-title":"Explainable AI for interpretation of ovarian tumor classification using enhanced ResNet50","volume":"14","author":"Guha","year":"2024","journal-title":"Diagnostics"},{"issue":"4","key":"10.1016\/j.engappai.2026.114512_b22","doi-asserted-by":"crossref","DOI":"10.3802\/jgo.2025.36.e53","article-title":"Early prediction and risk stratification of ovarian cancer based on clinical data using machine learning approaches","volume":"36","author":"Gui","year":"2025","journal-title":"J. Gynecol. Oncol."},{"key":"10.1016\/j.engappai.2026.114512_b23","article-title":"Transformers in medical imaging: A survey","volume":"81","author":"Han","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.engappai.2026.114512_b24","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.engappai.2026.114512_b25","article-title":"Medical image classification based on contour processing attention mechanism","volume":"169","author":"Jia","year":"2025","journal-title":"Comput. Biol. Med."},{"issue":"10","key":"10.1016\/j.engappai.2026.114512_b26","doi-asserted-by":"crossref","first-page":"3006","DOI":"10.1158\/1078-0432.CCR-18-3378","article-title":"Application of artificial intelligence for preoperative diagnostic and prognostic prediction in epithelial ovarian cancer based on blood biomarkers","volume":"25","author":"Kawakami","year":"2019","journal-title":"Clin. Cancer Res."},{"issue":"5","key":"10.1016\/j.engappai.2026.114512_b27","doi-asserted-by":"crossref","first-page":"543","DOI":"10.3390\/diagnostics14050543","article-title":"An empirical evaluation of a novel ensemble deep neural network model and explainable AI for accurate segmentation and classification of ovarian tumors using CT images","volume":"14","author":"Kodipalli","year":"2024","journal-title":"Diagnostics"},{"key":"10.1016\/j.engappai.2026.114512_b28","article-title":"Vision transformers in medical imaging: Current status and future prospects","author":"Kundu","year":"2023","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"4","key":"10.1016\/j.engappai.2026.114512_b29","first-page":"280","article-title":"Epithelial ovarian cancer: Evolution of management in the era of precision medicine","volume":"69","author":"Lheureux","year":"2019","journal-title":"CA: Cancer J. Clin."},{"key":"10.1016\/j.engappai.2026.114512_b30","article-title":"Transforming medical imaging with transformers? A comparative review of key properties, current progress, and future perspectives","volume":"84","author":"Li","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.engappai.2026.114512_b31","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"issue":"14","key":"10.1016\/j.engappai.2026.114512_b32","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.2174\/1573405618666220516122145","article-title":"Pattern classification for ovarian tumors by integration of radiomics and deep learning features","volume":"18","author":"Liu","year":"2022","journal-title":"Curr. Med. Imaging"},{"key":"10.1016\/j.engappai.2026.114512_b33","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S., 2022. A ConvNet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR, pp. 11976\u201311986.","DOI":"10.1109\/CVPR52688.2022.01167"},{"issue":"21","key":"10.1016\/j.engappai.2026.114512_b34","doi-asserted-by":"crossref","first-page":"15805","DOI":"10.1007\/s00521-023-08569-y","article-title":"A hybrid deep learning approach for detection and segmentation of ovarian tumours","volume":"35","author":"Maria","year":"2023","journal-title":"Neural Comput. Appl."},{"issue":"4","key":"10.1016\/j.engappai.2026.114512_b35","first-page":"911","article-title":"BAM: Block attention mechanism for OCT image classification","volume":"16","author":"Nabijiang","year":"2022","journal-title":"IET Image Process."},{"key":"10.1016\/j.engappai.2026.114512_b36","first-page":"32","article-title":"Ovarian cancer screening: Current status and future directions","volume":"65","author":"Nash","year":"2020","journal-title":"Best Pr. Res.: Clin. Obs. Gynaecol."},{"key":"10.1016\/j.engappai.2026.114512_b37","doi-asserted-by":"crossref","DOI":"10.5114\/pjr.2024.134817","article-title":"Deep learning in ovarian cancer diagnosis: A comprehensive review of various imaging modalities","volume":"89","author":"Sadeghi","year":"2024","journal-title":"Pol. J. Radiol."},{"key":"10.1016\/j.engappai.2026.114512_b38","article-title":"Cancer statistics, 2023","author":"Siegal","year":"2023","journal-title":"CA: Cancer J. Clin."},{"key":"10.1016\/j.engappai.2026.114512_b39","series-title":"Proceedings of the 36th International Conference on Machine Learning","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume":"vol. 97","author":"Tan","year":"2019"},{"issue":"7\u20138","key":"10.1016\/j.engappai.2026.114512_b40","first-page":"449","article-title":"Adnexal lesions: Imaging strategies for ultrasound and MR imaging","volume":"99","author":"Tsili","year":"2018","journal-title":"Diagn. Interv. Imaging"},{"key":"10.1016\/j.engappai.2026.114512_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2022.102093","article-title":"Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images","volume":"99","author":"Wang","year":"2022","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.engappai.2026.114512_b42","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S., 2018. CBAM: Convolutional block attention module. In: Proceedings of the European conference on computer vision. ECCV, pp. 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"10.1016\/j.engappai.2026.114512_b43","article-title":"ConvNeXt-based network for medical image segmentation: Applications and challenges","volume":"82","author":"Xu","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.engappai.2026.114512_b44","article-title":"Preoperative CT-based deep learning model for predicting overall survival in patients with high-grade serous ovarian cancer","volume":"12","author":"Zheng","year":"2022","journal-title":"Front. Oncol."},{"key":"10.1016\/j.engappai.2026.114512_b45","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.2147\/CMAR.S482837","article-title":"Predicting the recurrence of ovarian cancer based on machine learning","volume":"16","author":"Zhou","year":"2024","journal-title":"Cancer Manag. Res."},{"issue":"10","key":"10.1016\/j.engappai.2026.114512_b46","doi-asserted-by":"crossref","first-page":"1703","DOI":"10.3390\/diagnostics13101703","article-title":"A deep learning framework for the prediction and diagnosis of ovarian cancer in pre- and post-menopausal women","volume":"13","author":"Ziyambe","year":"2023","journal-title":"Diagnostics"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626007931?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626007931?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T22:13:52Z","timestamp":1775945632000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626007931"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":46,"alternative-id":["S0952197626007931"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114512","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Attention-driven ConvNeXt architecture with transformer-inspired encoding for early ovarian tumor diagnosis","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114512","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114512"}}