To realize the automatic diagnosis of cervical intraepithelial neoplasia (CIN) cases by preacetic acid test and postacetic acid test colposcopy images, this paper proposes a method of cervical precancerous lesion diagnosis based on multimodal feature changes. First, the preacetic acid test and postacetic acid test colposcopy images were registered based on cross-correlation and projection transformation, and then the cervical region was extracted by the k-means clustering algorithm. Finally, a deep learning network was used to extract features and classify the preacetic acid test and postacetic acid test cervical images after registration. Finally, the proposed method achieves a classification accuracy of 86.3%, a sensitivity of 84.1%, and a specificity of 89.8% in 60 test cases. Experimental results show that this method can make better use of the multimodal features of colposcopy images and has lower requirements for medical staff in the process of data acquisition. It has certain clinical significance in cervical cancer precancerous lesion screening systems.

为了通过醋酸前和醋酸后阴道镜检查图像自动诊断宫颈上皮内瘤变（CIN），提出了一种基于多峰特征变化的宫颈癌前病变诊断方法。首先，基于互相关和投影变换来记录前乙酸测试和后乙酸测试的阴道镜图像，然后通过k提取宫颈区域。-均值聚类算法。最后，在注册后，使用深度学习网络提取特征并对乙酸测试前和乙酸测试后的子宫颈图像进行分类。最终，该方法在60个测试案例中实现了86.3％的分类准确度，84.1％的灵敏度和89.8％的特异性。实验结果表明，该方法可以更好地利用阴道镜图像的多峰特征，对医务人员的数据采集要求较低。在宫颈癌癌前病变筛查系统中具有一定的临床意义。