{"title":"Cervical cytology screening using the fused deep learning architecture with attention mechanisms","authors":"","doi":"10.1016/j.asoc.2024.112202","DOIUrl":null,"url":null,"abstract":"<div><p>Cervical cancer remains a significant global health concern. Given the disparity between limited medical resources and the requisite professional personnel, the coverage of cervical screening is inadequate, particularly in underdeveloped areas. Computer-assisted liquid-based cytology diagnostic systems offer favorable solutions.</p><p>Detection of small nuclei within a complex liquid-based environment poses a challenge, exacerbated by the restricted availability of manual annotations. In this study, we propose FuseDLAM, a comprehensive computer-aided diagnostic system, which employs enhanced YOLOv8 with transformers for rapid localization of individual squamous epithelial cells. We leverage artificial intelligence-generated content techniques for data augmentation, effectively reducing the need for costly manual annotations. By integrating multiple deep convolutional neural network models with self-attention mechanisms, the system extracts crucial features from cell nuclei. These features are then fused through a fully connected layer to facilitate robust cell classification. FuseDLAM achieves an F1-score of 99.3<span><math><mo>%</mo></math></span> on the public SIPaKMeD dataset, demonstrating comparability with state-of-the-art approaches. It also proves its practical applicability in real-world clinical scenarios, achieving an F1-score of 91.2 % in identifying abnormal cervical squamous cells. Additionally, ablation experiments in both datasets validate the model's effectiveness. This underscores its potential for widespread application in medical imaging tasks.</p></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1568494624009761/pdfft?md5=ea022dd0aaf5f59046ca930eb119d0ad&pid=1-s2.0-S1568494624009761-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Soft Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1568494624009761","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Cervical cancer remains a significant global health concern. Given the disparity between limited medical resources and the requisite professional personnel, the coverage of cervical screening is inadequate, particularly in underdeveloped areas. Computer-assisted liquid-based cytology diagnostic systems offer favorable solutions.
Detection of small nuclei within a complex liquid-based environment poses a challenge, exacerbated by the restricted availability of manual annotations. In this study, we propose FuseDLAM, a comprehensive computer-aided diagnostic system, which employs enhanced YOLOv8 with transformers for rapid localization of individual squamous epithelial cells. We leverage artificial intelligence-generated content techniques for data augmentation, effectively reducing the need for costly manual annotations. By integrating multiple deep convolutional neural network models with self-attention mechanisms, the system extracts crucial features from cell nuclei. These features are then fused through a fully connected layer to facilitate robust cell classification. FuseDLAM achieves an F1-score of 99.3 on the public SIPaKMeD dataset, demonstrating comparability with state-of-the-art approaches. It also proves its practical applicability in real-world clinical scenarios, achieving an F1-score of 91.2 % in identifying abnormal cervical squamous cells. Additionally, ablation experiments in both datasets validate the model's effectiveness. This underscores its potential for widespread application in medical imaging tasks.
期刊介绍:
Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.
Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.