Hybrid feature fusion in cervical cancer cytology: a novel dual-module approach framework for lesion detection and classification using radiomics, deep learning, and reproducibility.

ABSTRACT:

Objective

Cervical cancer screening through cytology remains the gold standard for early detection, but manual analysis is time-consuming, labor-intensive, and prone to inter-observer variability. This study proposes an automated deep learning-based framework that integrates lesion detection, feature extraction, and classification to enhance the accuracy and efficiency of cytological diagnosis.

Materials and methods

A dataset of 4,236 cervical cytology samples was collected from six medical centers, with lesion annotations categorized into six diagnostic classes (NILM, ASC-US, ASC-H, LSIL, HSIL, SCC). Four deep learning models, Swin Transformer, YOLOv11, Faster R-CNN, and DETR (DEtection TRansformer), were employed for lesion detection, and their performance was compared usi

SUBMITTER: Niu S 

PROVIDER: S-EPMC12400031 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature