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

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 levels of specialist experience were compared. In total, 2,475 participants were recruited, and 13,084 colposcopy images were included in this study. The diagnostic accuracy of the Dense-U-Net model increased significantly with increasing colposcopy images per patient. As the number of images in the training set increased, the diagnostic accuracy of the Dense-U-Net model for cervical intraepithelial neoplasm 3 or worse (CIN3+) diagnosis increased (