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While deep learning can improve medical image analysis, cervical cells show subtle morphological differences and complex nuclear patterns that hinder automated models. This work introduces a dedicated deep learning architecture that combines cytopathology-tailored augmentation, a Morphology Attention Module for multi-scale adaptive fusion, and a Spatial-Channel Mixer for nuclear neighborhood encoding. Results on SIPaKMeD and Mendeley LBC achieve state-of-the-art accuracy with large error-rate reductions and robust generalization across cell types and imaging conditions.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/multiscale-feature-integration-with-enhanced-cytomorph-for-high-accuracy-cervical-cytology-classification-research-article/354301/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/multiscale-feature-integration-with-enhanced-cytomorph-for-high-accuracy-cervical-cytology-classification-research-article/354301.png","ImageObject",300,407,{"name":92,"@type":93},"McGucket","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is cervical cytology image classification important?","Question",{"text":112,"@type":113},"Accurate classification supports early detection and prevention of cervical cancer, improving screening outcomes and helping reduce deaths through timely identification of abnormalities.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What are the three main components of the proposed deep learning approach?",{"text":117,"@type":113},"The method uses a specialized data augmentation pipeline, a Morphology Attention Module for multi-scale cellular features with adaptive fusion, and a Spatial-Channel Mixer to encode nuclear neighborhood spatial information.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the model perform on the evaluated datasets?",{"text":121,"@type":113},"Experiments on SIPaKMeD and Mendeley LBC report state-of-the-art accuracy, including 99.06% on the 5-class SIPaKMeD dataset and 98.55% on the Mendeley LBC dataset, with substantial error-rate reductions versus conventional CNNs and recent Vision Transformers.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},354301,1790173718,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},1236954412713,"https://us-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","OPEN ACCESS  \nCitation: İlgazi Kılıç E, Kılıç Ş (2026) Multiscale feature integration with enhanced cytomorph for high-accuracy cervical cytology classification. PLoS One 21(6): e0351063 .  \n[https://doi.org/10.1371/journal.pone.0351063](https://doi.org/10.1371/journal.pone.0351063)  \nEditor: Wencheng Zhu, Tianjin University, CHINA  \nReceived: October 10, 2025  \nAccepted: May 21, 2026  \nPublished: June 10, 2026  \nCopyright: © 2026 İlgazi Kılıç, Kılıç . This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData availability statement: The datasets used in this study are publicly available from third-party repositories. The SIPaKMeD dataset (Cervical Cancer Largest Dataset) is available at Kaggle: [https://www.kaggle.com/datasets/prahl](https://www.kaggle.com/datasets/prahl)admehandiratta/cervical-cancer-largest-dataset-sipakmed The Liquid-Based Cytology (LBC) dataset is available at Mendeley Data: [https://](https://)  \nRESEARCH ARTICLE  \nMulti-scale feature integration with enhanced cytomorph for high-accuracy cervical cytology classification  \nElif İlgazi Kılıç1☯, Şafak Kılıç2,3☯*  \n1 Department of Obstetrics and Gynecology, Kayseri City Hospital, Kayseri, Türkiye 2 School of Computer Science, CHART Laboratory, University of Nottingham, Nottingham, United Kingdom, 3 Faculty of Engineering, Architecture and Design, Department of Software Engineering, Kayseri University, Kayseri, Türkiye  \n☯ These authors contributed equally to this work.  \n* [safakkilic@kayseri.edu.tr](safakkilic@kayseri.edu.tr)  \nAbstract  \nAccurate classification of cervical cytology images plays a crucial role in early detection and prevention of cervical cancer, which remains a significant global health challenge. Despite advancements in deep learning for medical image analysis, the unique characteristics of cervical cells, including subtle morphological differences and complex nuclear patterns, pose considerable challenges for automated classification systems. In this paper, we present a novel deep learning architecture specifically designed for cervical cytology image classification. Our approach integrates three key components: (1) a specialized data augmentation pipeline tailored for cytopathology images,(2) a Morphology Attention Module (MAM) that captures multi-scale cellular features with adaptive feature fusion, and (3) a Spatial-Channel Mixer (SCM) that efficiently encodes nuclear neighborhood spatial information. Extensive experiments on both the SIPaKMeD and Mendeley LBC datasets demonstrate the superior performance of our model, achieving state-of-the-art accuracy of 99.06% on the 5-class SIPaKMeD dataset and 98.55% on the Mendeley LBC dataset. Importantly, our approach reduces error rates by up to 82.5% compared to conventional CNN architectures and 61.8% compared to recent Vision Transformer approaches. The proposed architecture demonstrates robust generalization across different cell types and imaging conditions, making it a promising tool for enhancing cervical cancer screening programs. Our work contributes to the advancement of automated cytology analysis and has the potential to improve early detection of cervical abnormalities, particularly in resource-limited settings where expert cytopathologists may be scarce.  \nPLOS One | [https://doi.org/10.1371/journal.pone.0351063](https://doi.org/10.1371/journal.pone.0351063) June 10, 2026 1 / 32  \n[data.mendeley.com/datasets/zddtpgzv63/1](data.mendeley.com/datasets/zddtpgzv63/1)  \nThese datasets can be freely accessed and downloaded by any researcher without restriction. The authors did not receive any special access privileges. All data required to replicate the findings of this study are publicly available through the provided links.  \nFunding: The author(s) received no specific funding for this work.  \nCompetin","cbCairxJPj9Yal1Z","https://ap.wps.com/l/cbCairxJPj9Yal1Z","pdf",8628013,32,"English","# Abstract\n# Introduction\n## Background on cervical cancer and screening\n## Challenges in automated cytology classification\n# Methods (proposed study direction)","[{\"question\":\"Why is cervical cytology image classification important?\",\"answer\":\"Accurate classification supports early detection and prevention of cervical cancer, improving screening outcomes and helping reduce deaths through timely identification of abnormalities.\"},{\"question\":\"What are the three main components of the proposed deep learning approach?\",\"answer\":\"The method uses a specialized data augmentation pipeline, a Morphology Attention Module for multi-scale cellular features with adaptive fusion, and a Spatial-Channel Mixer to encode nuclear neighborhood spatial information.\"},{\"question\":\"How does the model perform on the evaluated datasets?\",\"answer\":\"Experiments on SIPaKMeD and Mendeley LBC report state-of-the-art accuracy, including 99.06% on the 5-class SIPaKMeD dataset and 98.55% on the Mendeley LBC dataset, with substantial error-rate reductions versus conventional CNNs and recent Vision Transformers.\"}]","Multiscale feature integration with enhanced cytomorph for high-accuracy cervical cytology classification - Research article | PDF",1790109955,81]