TY  - EJOU
AU  - Xu, Chuanyun 
AU  - Hu, Die 
AU  - Zhang, Yang 
AU  - Huang, Shuaiye 
AU  - Sun, Yisha 
AU  - Li, Gang 

TI  - Multi-Scale Feature Fusion Network for Accurate Detection of Cervical Abnormal Cells
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 1
SN  - 1546-2226

AB  - 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.
KW  - Cervical abnormal cells; image detection; multi-scale feature fusion; contextual information

DO  - 10.32604/cmc.2025.061579