MacNet: a mobile attention classification network combining convolutional neural network and transformer for the differentiation of cervical cancer.

ABSTRACT:

Background

Cervical cancer remains a critical global health issue, responsible for over 600,000 new cases and 300,000 deaths annually. Pathological imaging of cervical cancer is a crucial diagnostic tool. However, distinguishing specific areas of cellular differentiation remains challenging because of the lack of clear boundaries between cells at various stages of differentiation. To address the limitations of conventional clinical and deep learning (DL) methods, we developed a mobile attention classification network (MacNet) with multiscale features, aiming to increase the accuracy of differentiation classification and quantitatively analyze cervical cancer cell differentiation.

Methods

We investigated the application of MacNet for classifying non-background images into 3 s

SUBMITTER: An Y 

PROVIDER: S-EPMC11744112 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature