Background

Cervical cancer is the second most common female cancer globally, and it is vital to detect cervical cancer with low cost at an early stage using automated screening methods of high accuracy, especially in areas with insufficient medical resources. Automatic detection of cervical intraepithelial neoplasia (CIN) can effectively prevent cervical cancer.

Objectives

Due to the deficiency of standard and accessible colposcopy image datasets, we present a dataset containing 4753 colposcopy images acquired from 679 patients in three states (acetic acid reaction, green filter, and iodine test) for detection of cervical intraepithelial neoplasia. Based on this dataset, a new computer-aided method for cervical cancer screening was proposed.

Methods

We employed a wide range of methods to comprehensively evaluate our proposed dataset. Hand-crafted feature extraction methods and deep learning methods were used for the performance verification of the multistate colposcopy image (MSCI) dataset. Importantly, we propose a gated recurrent convolutional neural network (C-GCNN) for colposcopy image analysis that considers time series and combined multistate cervical images for CIN grading.

Results

The experimental results showed that the proposed C-GCNN model achieves the best classification performance in CIN grading compared with hand-crafted feature extraction methods and classic deep learning methods. The results showed an accuracy of 96.87 %, a sensitivity of 95.68 %, and a specificity of 98.72 %.

Conclusion

A multistate colposcopy image dataset (MSCI) is proposed. A CIN grading model (C-GCNN) based on the MSCI dataset is established, which provides a potential method for automated cervical cancer screening.

中文翻译：

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MSCI：用于宫颈癌筛查的阴道镜图像分类的多状态数据集

背景

宫颈癌是全球第二大最常见的女性癌症，使用高精度的自动筛查方法及早发现低成本的宫颈癌至关重要，尤其是在医疗资源不足的地区。自动检测宫颈上皮内瘤变（CIN）可以有效预防宫颈癌。

目标

由于缺乏标准且易于使用的阴道镜图像数据集，我们提出了一个数据集，其中包含从679个患者中获得的4753幅阴道镜图像，这三种状态用于检测宫颈上皮内瘤样变（乙酸反应，绿色滤光片和碘测试）。基于该数据集，提出了一种新的计算机辅助宫颈癌筛查方法。

方法

我们采用了多种方法来全面评估我们提出的数据集。手工制作的特征提取方法和深度学习方法用于多状态阴道镜图像（MSCI）数据集的性能验证。重要的是，我们提出了一种用于阴道镜图像分析的门控循环卷积神经网络（C-GCNN），该网络考虑了时间序列并结合了多状态宫颈图像进行CIN分级。

结果

实验结果表明，与手工特征提取方法和经典深度学习方法相比，本文提出的C-GCNN模型在CIN分级中具有最佳分类性能。结果显示精度为96.87％，灵敏度为95.68％，特异性为98.72％。

结论

提出了一种多状态阴道镜图像数据集（MSCI）。建立了基于MSCI数据集的CIN分级模型（C-GCNN），为宫颈癌的自动筛查提供了一种潜在的方法。