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Classification and diagnosis of cervical lesions based on colposcopy images using deep fully convolutional networks:
Binhua Dong1,2, Huifeng Xue3, Ye Li1
1Laboratory of Gynecologic Oncology, Department of Gynecology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou 350001, China.
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 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 (P = 0.035). The rate of diagnostic accuracy (0.89 vs 0.85, P < 0.001) of CIN3+ lesions using the Dense-U-Net model was higher than that of expert colposcopists, and the missed diagnosis (0.06 vs 0.07, P = 0.002) and false positive (0.05 vs 0.08, P < 0.001) were lower. Moreover, Dense-U-Net is more accurate in diagnosing the type III cervical transformation zone, which is difficult to diagnose by experts (P < 0.001). The Dense-U-Net model also showed higher diagnostic accuracy for CIN3+ in an independent test set (P < 0.001). To diagnose the same 870 test images, the Dense-U-Net system took 1.76 ± 0.09 min, while the expert, senior, and junior colposcopists took 716.3 ± 49.76, 892.1 ± 92.30, and 3034.7 ± 259.51 min, respectively. The study successfully built a reliable, quick, and effective Dense-U-Net model to assist with colposcopy examinations.
