Abstract Traditional screening of cervical cells largely depends on the experience of pathologists, which also has the problem of low accuracy and poor efficiency. Medical image processing combining deep learning and machine learning shows its superiority in the field of cell classification. A new framework based on strong feature Convolutional Neural Networks (CNN)-Support Vector Machine (SVM) model was proposed to accurately classify the cervical cells. A method fusing the strong features extracted by Gray-Level Co-occurrence Matrix (GLCM) and Gabor with abstract features from the hidden layers of CNN was conducted, meanwhile the fused ones were input into the SVM for classification. An effective dataset amplification method was designed to improve the robustness of the model. The proposed method was evaluated on two independent datasets with the metrics of accuracy (Acc), sensitivity (Sn), and specificity (Sp). Our approach outperformed than the state-of-the-art models with the Acc, Sn, and Sp of 99.3, 98.9, 99.4 for 2-class detection in the mass, respectively. The results indicated that the strong feature CNN-SVM model could be applied in cell classification for the early screening of cervical cancer.

中文翻译：

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基于强特​​征CNN-SVM网络的宫颈癌细胞检测

摘要 传统的宫颈细胞筛查很大程度上依赖病理学家的经验，也存在准确率低、效率低的问题。结合深度学习和机器学习的医学图像处理在细胞分类领域显示出其优越性。提出了一种基于强特征卷积神经网络（CNN）-支持向量机（SVM）模型的新框架来准确分类宫颈细胞。将灰度共生矩阵(GLCM)和Gabor提取的强特征与CNN隐藏层的抽象特征进行融合，同时将融合后的特征输入SVM进行分类。设计了一种有效的数据集放大方法来提高模型的鲁棒性。所提出的方法在两个独立的数据集上进行了评估，分别是准确度 (Acc)、灵敏度 (Sn) 和特异性 (Sp)。对于质量中的 2 类检测，我们的方法优于最先进的模型，其中 Acc、Sn 和 Sp 分别为 99.3、98.9、99.4。结果表明，强特征CNN-SVM模型可应用于细胞分类，用于宫颈癌的早期筛查。