Optimization research on multi-classification of cervical cell images integrating attention mechanism and transfer learning
YANG Chen
HU Xiaopeng
Abstract：Aiming at the problems of high similarity of morphological features of similar categories of cervical cell images and insuf-ficient generalization ability of the model caused by the limited size of the dataset,we proposed an improved Resnet34 model for the multi-classification task of cervical cell images.Firstly,an attention module was introduced into the residual blocks of Resnet34,and the activation function ReLU was replaced with PReLU,thereby enhancing the model's ability to capture details and its adaptability.Secondly,the transfer learning strategy was adopted to enhance the generalization performance of the model.The result showed that the accuracy of the improved model reached 96.78%,was 1.32%higher than that of the original Resnet34.The research can effectively capture the subtle differences in the image morphology of cervical cells belonging to similar categories,optimize the recognition perform-ance and generalization ability of the model,can provide reliable technical support for the early automated screening of cervical cancer.
Keywords：Multi-classificationCervical cell imagesResnet34Attention mechanismTransfer learning
Publication Date：2026-06-30
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：7( 197-203 )