High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt

Abstract

Traditional cervical cancer diagnosis mainly relies on human papillomavirus (HPV) concentration testing. Considering that HPV concentrations vary from individual to individual and fluctuate over time, this method requires multiple tests, leading to high costs. Recently, some scholars have focused on the method of cervical cytology for diagnosis. However, cervical cancer cells have complex textural characteristics and small differences between different cell subtypes, which brings great challenges for high-precision screening of cervical cancer. In this paper, we propose a high-precision cervical cancer precancerous lesion screening classification method based on ConvNeXt, utilizing self-supervised data augmentation and ensemble learning strategies to achieve cervical cancer cell feature extraction and inter-class discrimination, respectively. We used the Deep Cervical Cytological Levels (DCCL) dataset, which includes 1167 cervical cytology specimens from participants aged 32 to 67, for algorithm training and validation. We tested our method on the DCCL dataset, and the final classification accuracy was 8.85% higher than that of previous advanced models, which means that our method has significant advantages compared to other advanced methods.

FMA-Net: Fusion of Multi-Scale Attention for Grading Cervical Precancerous Lesions

“…Most studies on the classification of cervical precancerous lesions are based on cervical tissue biopsies [14][15][16], while the research on colposcopic images is relatively limited. We believe that there are two main reasons for this phenomenon: first, the lack of relevant datasets and difficulties in collecting related images, and second, the similarity between precancerous cervical lesions and other diseases, making it challenging to distinguish them accurately.…”