Cervical cancer is a major gynecological malignant tumor that threatens women's health. Current cytological methods have certain limitations for cervical cancer early screening. Light scattering patterns can reflect small differences in the internal structure of cells. In this study, we develop a light scattering pattern specific convolutional network (LSPS-net) based on deep learning algorithm and integrate it into a 2D light scattering static cytometry for automatic, label-free analysis of single cervical cells. An accuracy rate of 95.46% for the classification of normal cervical cells and cancerous ones (mixed C-33A and CaSki cells) is obtained. When applied for the subtyping of label-free cervical cell lines, we obtain an accuracy rate of 93.31% with our LSPS-net cytometric technique. Furthermore, the three-way classification of the above different types of cells has an overall accuracy rate of 90.90%, and comparisons with other feature descriptors and classification algorithms show the superiority of deep learning for automatic feature extraction. The LSPS-net static cytometry may potentially be used for cervical cancer early screening, which is rapid, automatic and label-free.

中文翻译：

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用于宫颈细胞无标记分类的光散射模式特定卷积网络静态细胞计数

宫颈癌是威胁女性健康的主要妇科恶性肿瘤。目前的细胞学方法对宫颈癌早期筛查有一定的局限性。光散射模式可以反映细胞内部结构的微小差异。在这项研究中，我们开发了一种基于深度学习算法的光散射模式特定卷积网络 (LSPS-net)，并将其集成到 2D 光散射静态细胞仪中，用于对单个宫颈细胞进行自动、无标记分析。获得了对正常宫颈细胞和癌变细胞（混合 C-33A 和 CaSki 细胞）进行分类的准确率为 95.46%。当应用于无标记宫颈细胞系的亚型分型时，我们使用我们的 LSPS-net 细胞计数技术获得了 93.31% 的准确率。此外，上述不同类型细胞的三向分类总体准确率为90.90%，与其他特征描述子和分类算法的比较显示了深度学习在自动特征提取方面的优越性。LSPS-net 静态细胞计数法可能用于宫颈癌早期筛查，它是快速、自动和无标记的。