The diagnosis of cervical dysplasia, carcinoma in situ and confirmed carcinoma cases is more easily perceived by commercially available and current research-based decision support systems when the scenario of pathologists to patient ratio is small. The treatment modalities for such diagnosis rely exclusively on precise identification of dysplasia stages as followed by The Bethesda System. The classification based on The Bethesda System is a multiclass problem, which is highly relevant and vital. Reliance on image interpretation, when done manually, introduces inter-observer variability and makes the microscope observation tedious and time-consuming. Taking this into account, a computer-assisted screening system built on deep learning can significantly assist pathologists to screen with correct predictions at a faster rate. The current study explores six different deep convolutional neural networks- Alexnet, Vggnet (vgg-16 and vgg-19), Resnet (resnet-50 and resnet-101) and Googlenet architectures for multi-class (four-class) diagnosis of cervical pre-cancerous as well as cancer lesions and incorporates their relative assessment. The study highlights the addition of an ensemble classifier with three of the best deep learning models for yielding a high accuracy multi-class classification. All six deep models including ensemble classifier were trained and validated on a hospital-based pap smear dataset collected through both conventional and liquid-based cytology methods along with the benchmark Herlev dataset.

中文翻译：

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使用基于集合的深度卷积神经网络基于融合的决策，对宫颈涂片图像的多类宫颈癌诊断预测进行全面研究。

当病理学家与患者的比例很小时，可商购的和当前基于研究的决策支持系统更容易识别宫颈发育异常，原位癌和确诊的癌症病例。这种诊断的治疗方式仅取决于对不典型增生阶段的精确识别，然后是贝塞斯达系统。基于贝塞斯达系统的分类是一个多类问题，具有高度相关性和重要性。依靠人工进行图像解释会导致观察者之间的差异，并使显微镜观察变得乏味且耗时。考虑到这一点，基于深度学习的计算机辅助筛选系统可以极大地帮助病理学家更快地筛查正确的预测。当前的研究探索了六个不同的深度卷积神经网络-Alexnet，Vggnet（vgg-16和vgg-19），Resnet（resnet-50和resnet-101）和Googlenet架构，用于多级（四级）宫颈癌的诊断-癌和癌病灶，并纳入其相对评估。这项研究着重介绍了集成分类器与三个最佳深度学习模型的结合，以产生高精度的多分类。包括集合分类器在内的所有六个深层模型均在通过常规和基于液体的细胞学方法以及基准Herlev数据集收集的基于医院的宫颈涂片数据集上进行了培训和验证。Resnet（resnet-50和resnet-101）和Googlenet体系结构可对宫颈癌前病变和癌病变进行多级（四级）诊断，并结合了它们的相对评估。这项研究着重介绍了集成分类器与三个最佳深度学习模型的结合，以产生高精度的多分类。包括集合分类器在内的所有六个深层模型均在通过常规和基于液体的细胞学方法以及基准Herlev数据集收集的基于医院的宫颈涂片数据集上进行了培训和验证。Resnet（resnet-50和resnet-101）和Googlenet体系结构可对宫颈癌前病变和癌病变进行多级（四级）诊断，并结合了它们的相对评估。这项研究着重介绍了集成分类器与三个最佳深度学习模型的结合，以产生高精度的多分类。包括集合分类器在内的所有六个深层模型均在通过常规和基于液体的细胞学方法以及基准Herlev数据集收集的基于医院的宫颈涂片数据集上进行了培训和验证。这项研究着重介绍了集成分类器与三个最佳深度学习模型的结合，以产生高精度的多分类。包括集合分类器在内的所有六个深层模型均在通过常规和基于液体的细胞学方法以及基准Herlev数据集收集的基于医院的宫颈涂片数据集上进行了培训和验证。这项研究着重介绍了集成分类器与三个最佳深度学习模型的结合，以产生高精度的多分类。包括集合分类器在内的所有六个深层模型均在通过常规和基于液体的细胞学方法以及基准Herlev数据集收集的基于医院的宫颈涂片数据集上进行了培训和验证。