Classification and diagnosis of cervical lesions based on colposcopy images using deep fully convolutional networks: A man-machine comparison cohort study.

ABSTRACT: Colposcopy is an important technique in the diagnosis of cervical cancer. The development of computer-aided diagnosis methods can mitigate the shortage of colposcopists and improve the accuracy and efficiency of colposcopy examinations in China. This study proposes the Dense-U-Net model for colposcopy image recognition. This was a man-machine comparison cohort study. It presents a novel artificial intelligence (AI) model for the diagnosis of cervical lesions through colposcopy images using a Dense-U-Net image semantic segmentation algorithm. The Dense-U-Net model was created by applying the methods of "deepening the network structure," "applying dropout" and "max pooling." Moreover, image-based and population-based diagnostic performances of the AI algorithm and physicians with different l

SUBMITTER: Dong B 

PROVIDER: S-EPMC11955021 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature