VTCNet: A Feature Fusion DL Model Based on CNN and ViT for the Classification of Cervical Cells

Abstract

Cervical cancer is a common malignancy worldwide with high incidence and mortality rates in underdeveloped countries. The Pap smear test, widely used for early detection of cervical cancer, aims to minimize missed diagnoses, which sometimes results in higher false-positive rates. To enhance manual screening practices, computer-aided diagnosis (CAD) systems based on machine learning (ML) and deep learning (DL) for classifying cervical Pap cells have been extensively researched. In our study, we introduced a DL-based method named VTCNet for the task of cervical cell classification. Our approach combines CNN-SPPF and ViT components, integrating modules like Focus and SeparableC3, to capture more potential information, extract local and global features, and merge them to enhance classification performance. We evaluated our method on the public SIPaKMeD dataset, achieving accuracies, precision, recall, and F1 scores of 97.16%, 97.22%, 97.19%, and 97.18%, respectively. We also conducted additional experiments on the Herlev dataset, where our results outperformed previous methods. The VTCNet method achieved higher classification accuracy than traditional ML or shallow DL models through this integration. Related codes: https://github.com/Camellia-0892/VTCNet/tree/main.