Cervical cancer affects millions of women worldwide and has a significantly higher survival rate when diagnosed early. Pap smears and cervical biopsies are vital screening tools for detecting such cancer. However, the success of these screening processes depends on the skills of cytologists. A recent trend in diagnostic cytology is to apply machine-learning-based models to classify cancer using cell images. These automated models have been shown to perform just as well as, or even better than, expert cytologists. Some notable methods for classifying cervix cancers include ResNet50, VGG16, MobileNetV2, and InceptionV3, based on deep convolutional neural networks (CNN). However, these methods are computationally expensive. We present CerviXpert, a multi-structural Convolutional Neural Network, to identify cervix cancer. We perform extensive experiments on a publicly available dataset, SiPaKMeD, to show the efficacy of our method. CerviXpert presents a promising solution for efficient cervical cancer screening and diagnosis by striking a balance between accuracy and practical feasibility.

翻译：宫颈癌影响着全球数百万女性，早期诊断可显著提高其生存率。巴氏涂片和宫颈活检是检测此类癌症的重要筛查工具。然而，这些筛查过程的成功与否取决于细胞学家的专业技能。诊断细胞学领域的一个最新趋势是应用基于机器学习的模型，通过细胞图像对癌症进行分类。这些自动化模型已被证明与专家细胞学家表现相当，甚至更优。一些基于深度卷积神经网络（CNN）的著名宫颈癌分类方法包括ResNet50、VGG16、MobileNetV2和InceptionV3。然而，这些方法计算成本高昂。我们提出了CerviXpert，一种多结构卷积神经网络，用于识别宫颈癌。我们在公开数据集SiPaKMeD上进行了大量实验，以证明我们方法的有效性。CerviXpert在准确性与实际可行性之间取得了平衡，为高效的宫颈癌筛查与诊断提供了一个前景广阔的解决方案。