{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T06:32:24Z","timestamp":1786689144767,"version":"build-2736575974"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Array"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.array.2026.101102","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T23:42:49Z","timestamp":1784590969000},"page":"101102","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["XQE-Net: A deep learning framework with integrated feature fusion and optimization for cervical cancer screening"],"prefix":"10.1016","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1179-4956","authenticated-orcid":false,"given":"Bhawna","family":"Swarnkar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nilay","family":"Khare","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manasi","family":"Gyanchandani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nitin","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.array.2026.101102_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115642","article-title":"An ensemble method for nuclei detection of overlapping cervical cells","volume":"185","author":"Diniz","year":"2021","journal-title":"Expert Syst Appl"},{"issue":"8","key":"10.1016\/j.array.2026.101102_bib2","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1002\/ijc.31937","article-title":"Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods","volume":"144","author":"Ferlay","year":"2019","journal-title":"Int J Cancer"},{"issue":"5","key":"10.1016\/j.array.2026.101102_bib3","first-page":"321","article-title":"Cervical cancer screening for individuals at average risk: 2020 guideline update from the American Cancer society","volume":"70","author":"Fontham","year":"2020","journal-title":"CA Cancer J Clin"},{"key":"10.1016\/j.array.2026.101102_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106574","article-title":"Interpretable pap-smear image retrieval for cervical cancer detection with rotation invariance mask generation deep hashing","author":"\u00d6zbay","year":"2023","journal-title":"Comput Biol Med"},{"issue":"3","key":"10.1016\/j.array.2026.101102_bib5","first-page":"209","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J Clin"},{"issue":"2","key":"10.1016\/j.array.2026.101102_bib6","doi-asserted-by":"crossref","first-page":"e191","DOI":"10.1016\/S2214-109X(19)30482-6","article-title":"Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis","volume":"8","author":"Arbyn","year":"2020","journal-title":"Lancet Global Health"},{"key":"10.1016\/j.array.2026.101102_bib7","doi-asserted-by":"crossref","first-page":"484","DOI":"10.3389\/fphar.2019.00484","article-title":"Cervical cancer, different treatments and importance of bile acids as therapeutic agents in this disease","volume":"10","author":"\u0160arenac","year":"2019","journal-title":"Front Pharmacol"},{"key":"10.1016\/j.array.2026.101102_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2019.112951","article-title":"A fully-automated deep learning pipeline for cervical cancer classification","volume":"141","author":"Alyafeai","year":"2020","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.array.2026.101102_bib9","series-title":"2023 25th international multitopic conference (INMIC)","first-page":"1","article-title":"\"Automatic Detection of Diabetic Retinopathy from Fundus Images using Machine Learning Based Approaches,\"","author":"Bangyal","year":"2023"},{"key":"10.1016\/j.array.2026.101102_bib10","series-title":"2018 ISTAfrica week conference (IST-Africa)","article-title":"A review of applications of image analysis and machine learning techniques in automated diagnosis and classification of cervical cancer from pap-smear images","author":"William","year":"2018"},{"key":"10.1016\/j.array.2026.101102_bib11","series-title":"Artificial Intelligence in Cancer Diagnosis and Prognosis, volume 2: breast and bladder cancer","article-title":"AUTO-BREAST: a fully automated pipeline for breast cancer diagnosis using AI technology","author":"Ghanem","year":"2022"},{"key":"10.1016\/j.array.2026.101102_bib12","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2025.1717913","article-title":"Gender-based Alzheimer's detection using ResNet-50 and binary dragonfly algorithm on neuroimaging","volume":"8","author":"Haq","year":"2025","journal-title":"Front Artif Intell"},{"key":"10.1016\/j.array.2026.101102_bib13","article-title":"A survey of convolutional neural networks: analysis, applications, and prospects","author":"Li","year":"2021","journal-title":"IEEE Transact Neural Networks Learn Syst"},{"key":"10.1016\/j.array.2026.101102_bib14","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2025.3603458","article-title":"Systematic review of deep learning techniques for gynecological cancer diagnosis","author":"Swarnkar","year":"2025","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.array.2026.101102_bib15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J Big Data"},{"key":"10.1016\/j.array.2026.101102_bib16","article-title":"Octnet: a lightweight cnn for retinal disease classification from optical coherence tomography images","volume":"200","author":"Sunija","year":"2021","journal-title":"Comput Methods Progr Biomed"},{"key":"10.1016\/j.array.2026.101102_bib17","series-title":"2018 international conference on innovation and intelligence for informatics, computing, and technologies (3ICT)","first-page":"1","article-title":"\"Evolving Artificial Neural Networks Using Opposition Based Particle Swarm Optimization Neural Network for Data Classification,\"","author":"Bangyal","year":"2018"},{"issue":"4","key":"10.1016\/j.array.2026.101102_bib21","doi-asserted-by":"crossref","DOI":"10.1002\/cpe.6608","article-title":"Pap smear based cervical cancer detection using residual neural networks deep learning architecture","volume":"34","author":"Sellamuthu Palanisamy","year":"2022","journal-title":"Concurrency Comput Pract Ex"},{"key":"10.1016\/j.array.2026.101102_bib18","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.patrec.2019.11.015","article-title":"Selective feature connection mechanism: concatenating multi-layer CNN features with a feature selector","volume":"129","author":"Du","year":"2020","journal-title":"Pattern Recognit Lett"},{"issue":"4","key":"10.1016\/j.array.2026.101102_bib19","doi-asserted-by":"crossref","first-page":"12490","DOI":"10.1109\/TCE.2025.3606753","article-title":"\"Trustworthy Load Forecasting With Generative AI: a Dual-Attention ConvLSTM and VAE-Based Approach,\"","volume":"71","author":"Ali","year":"2025","journal-title":"IEEE Trans Consum Electron"},{"key":"10.1016\/j.array.2026.101102_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.103428","article-title":"Exemplar pyramid deep feature extraction based cervical cancer image classification model using pap-smear images","volume":"73","author":"Yaman","year":"2022","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.array.2026.101102_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.103014","article-title":"Cell classification with worse-case boosting for intelligent cervical cancer screening","volume":"91","author":"Song","year":"2024","journal-title":"Med Image Anal"},{"key":"10.1016\/j.array.2026.101102_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2022.108292","article-title":"Modified metaheuristics with stacked sparse denoising autoencoder model for cervical cancer classification","volume":"103","author":"Vaiyapuri","year":"2022","journal-title":"Comput Electr Eng"},{"key":"10.1016\/j.array.2026.101102_bib24","article-title":"Diagnosis of cervical cancer using CNN deep learning model with transfer learning approaches","volume":"105","author":"Kumar Sharma","year":"2025","journal-title":"Biomed Signal Process Control"},{"issue":"10","key":"10.1016\/j.array.2026.101102_bib25","doi-asserted-by":"crossref","first-page":"578","DOI":"10.3390\/bioengineering9100578","article-title":"Cervical net: a novel cervical cancer classification using feature fusion","volume":"9","author":"Alquran","year":"2022","journal-title":"Bioengineering"},{"key":"10.1016\/j.array.2026.101102_bib26","series-title":"Information Technology in Biomedicine: 9th International Conference, ITIB 2022 Kamie'\u0144Sl\u0105ski, Poland, June 20\u201322, 2022 proceedings","first-page":"285","article-title":"DVT: application of deep visual transformer in cervical cell image classification","author":"Liu","year":"2022"},{"key":"10.1016\/j.array.2026.101102_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106776","article-title":"A fuzzy distance-based ensemble of deep models for cervical cancer detection","volume":"219","author":"Pramanik","year":"2022","journal-title":"Comput Methods Progr Biomed"},{"issue":"1","key":"10.1016\/j.array.2026.101102_bib28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-93783-8","article-title":"A fuzzy rank-based ensemble of CNN models for classification of cervical cytology","volume":"11","author":"Manna","year":"2021","journal-title":"Sci Rep"},{"issue":"Part B","key":"10.1016\/j.array.2026.101102_bib29","article-title":"An amalgamation of deep neural networks optimized with Salp swarm algorithm for cervical cancer detection","volume":"123","author":"Bilal","year":"2025","journal-title":"Comput Electr Eng"},{"key":"10.1016\/j.array.2026.101102_bib30","series-title":"2024 IEEE international students' conference on electrical, electronics and computer science (SCEECS)","first-page":"1","article-title":"\"Enhancing Endometrial Tumor Detection: early Diagnosis with Advanced Vision Transformer Architecture,\"","author":"Swarnkar","year":"2024"},{"key":"10.1016\/j.array.2026.101102_bib31","series-title":"2024 IEEE international students' conference on electrical, electronics and computer science (SCEECS)","first-page":"1","article-title":"\"Early Diagnosis of Endometrial Cancer: an Ensemble-Based Deep Learning Approach,\"","author":"Swarnkar","year":"2024"},{"key":"10.1016\/j.array.2026.101102_bib32","series-title":"2018 25th IEEE international conference on image processing (ICIP)","first-page":"3144","article-title":"SIPAKMED: a new dataset for feature and image based classification of normal and pathological cervical cells in Pap smear images","author":"Plissiti","year":"2018"},{"key":"10.1016\/j.array.2026.101102_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2020.105589","article-title":"Liquid based-cytology pap smear dataset for automated multi-class diagnosis of pre-cancerous and cervical cancer lesions","volume":"30","author":"Hussain","year":"2020","journal-title":"Data Brief"},{"key":"10.1016\/j.array.2026.101102_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102076","article-title":"Discrete wavelet transform based data representation in deep neural network for gait abnormality detection","volume":"62","author":"Chakraborty","year":"2020","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.array.2026.101102_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.116954","article-title":"Filter group delays equalization for 2D discrete wavelet transform applications","volume":"200","author":"Bahoura","year":"2022","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.array.2026.101102_bib36","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.bspc.2017.07.022","article-title":"Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction","volume":"39","author":"Alickovic","year":"2018","journal-title":"Biomedical Signal Processing and C1ontrol"},{"key":"10.1016\/j.array.2026.101102_bib37","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"9225","article-title":"What makes transfer learning work for medical images: feature reuse & other factors","author":"Matsoukas","year":"2022"},{"key":"10.1016\/j.array.2026.101102_bib44","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102076","article-title":"Discrete wavelet transform based data representation in deep neural network for gait abnormality detection","volume":"62","author":"Chakraborty","year":"2020","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.array.2026.101102_bib38","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109293","article-title":"Applications of dynamic feature selection and clustering methods to medical diagnosis","volume":"126","author":"Ershadi","year":"2022","journal-title":"Appl Soft Comput"},{"issue":"1","key":"10.1016\/j.array.2026.101102_bib39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-016-1423-9","article-title":"Minimum redundancy maximum relevance feature selection approach for temporal gene expression data","volume":"18","author":"Radovic","year":"2017","journal-title":"BMC Bioinf"},{"key":"10.1016\/j.array.2026.101102_bib40","first-page":"1","article-title":"A new non-adaptive optimization method: stochastic gradient descent with momentum and difference","author":"Yuan","year":"2022","journal-title":"Appl Intell"},{"key":"10.1016\/j.array.2026.101102_bib41","first-page":"1139","article-title":"On the importance of initialization and momentum in deep learning","author":"Sutskever","year":"2013","journal-title":"International Conference on Machine Learning"},{"key":"10.1016\/j.array.2026.101102_bib42","series-title":"International conference on machine learning","first-page":"448","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015"},{"key":"10.1016\/j.array.2026.101102_bib43","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2024.105193","article-title":"Enhancing cervical cancer diagnosis: integrated attention-transformer system with weakly supervised learning","volume":"149","author":"Khowaja","year":"2024","journal-title":"Image Vis Comput"}],"container-title":["Array"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S259000562600425X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S259000562600425X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T06:15:53Z","timestamp":1786688153000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S259000562600425X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":44,"alternative-id":["S259000562600425X"],"URL":"https:\/\/doi.org\/10.1016\/j.array.2026.101102","relation":{},"ISSN":["2590-0056"],"issn-type":[{"value":"2590-0056","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"XQE-Net: A deep learning framework with integrated feature fusion and optimization for cervical cancer screening","name":"articletitle","label":"Article Title"},{"value":"Array","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.array.2026.101102","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"101102"}}