Multi-Scale Feature Fusion Network for Accurate Detection of Cervical Abnormal Cells  

作　　者：Chuanyun Xu Die Hu Yang Zhang Shuaiye Huang Yisha Sun Gang Li 

机构地区：[1]School of Computer and Information Science,Chongqing Normal University,Chongqing,401331,China [2]School of Artificial Intelligence,Chongqing University of Technology,Chongqing,401331,China

出　　处：《Computers, Materials & Continua》2025年第4期559-574,共16页计算机、材料和连续体(英文)

基　　金：funded by the China Chongqing Municipal Science and Technology Bureau,grant numbers 2024TIAD-CYKJCXX0121,2024NSCQ-LZX0135;Chongqing Municipal Commission of Housing and Urban-Rural Development,grant number CKZ2024-87;the Chongqing University of Technology graduate education high-quality development project,grant number gzlsz202401;the Chongqing University of Technology-Chongqing LINGLUE Technology Co.,Ltd.,Electronic Information(Artificial Intelligence)graduate joint training base;the Postgraduate Education and Teaching Reform Research Project in Chongqing,grant number yjg213116;the Chongqing University of Technology-CISDI Chongqing Information Technology Co.,Ltd.,Computer Technology graduate joint training base.

摘　　要：Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.

关 键 词：Cervical abnormal cells image detection multi-scale feature fusion contextual information 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]