A dual-branch deep learning network for circulating tumor cells classification.

ABSTRACT:

Background

Circulating tumor cells (CTCs) in peripheral blood are crucial for prognosis, treatment response, disease monitoring, and personalized therapy. However, identifying CTCs remains challenging due to their scarcity and heterogeneity, even with advanced deep learning models.

Methods

This study introduces an innovative hybrid framework combining a dual-branch network with traditional image processing techniques and automated CTC identification. By incorporating image and fluorescence attributes, the framework enhances feature representation robustness. Performance was evaluated using accuracy, precision, and recall metrics and comparisons with pathologists' manual counting.

Results

The framework achieved 97.05% accuracy in distinguishing CTCs from non-CTCs, with

SUBMITTER: Han C 

PROVIDER: S-EPMC12462185 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature