BiNext-Cervix: A novel hybrid model combining BiFormer and ConvNext for Pap smear classification

Abstract

Cervical cancer is the fourth most prevalent cancer among women worldwide and a major contributor to cancer-related mortality in females. Manually classifying cytopathology screening slides remains one of the most important and commonly used methods for diagnosing cervical cancer. However, this method requires the participation of medical experts and is highly labor intensive. Consequently, in regions with limited medical resources, prompt cervical cancer diagnosis is challenging. To address this issue, the BiNext-Cervix model, a new deep learning framework, has been proposed to rapidly and accurately diagnose cervical cancer via Pap smear images. BiNext-Cervix employs Tokenlearner in the initial stage to facilitate interaction between two pixels within the image, enabling the subsequent network to better understand the image features. Additionally, the BiNext-Cervix integrates the recently introduced ConvNext and BiFormer models, allowing for deep exploration of image information from both local and global perspectives. A fully connected layer is used to fuse the extracted features and perform the classification. The experimental results demonstrate that combining ConvNext and BiFormer achieves higher accuracy than using either model individually. Furthermore, the proposed BiNext-Cervix outperforms other commonly used deep learning models, showing superior performance.