Cervical intraepithelial neoplasia (CIN) and cervical cancer are major health problems faced by women worldwide. The conventional Papanicolaou (Pap) smear analysis is an effective method to diagnose cervical pre-malignant and malignant conditions by analyzing swab images. Various computer vision techniques can be explored to identify potential precancerous and cancerous lesions by analyzing the Pap smear image. The majority of existing work cover binary classification approaches using various classifiers and Convolution Neural Networks. However, they suffer from inherent challenges for minute feature extraction and precise classification. We propose a novel methodology to carry out the multiclass classification of cervical cells from Whole Slide Images (WSI) with optimum feature extraction. The actualization of Conv Net with Transfer Learning technique substantiates meaningful Metamorphic Diagnosis of neoplastic and pre-neoplastic lesions. As the Progressive Resizing technique (an advanced method for training ConvNet) incorporates prior knowledge of the feature hierarchy and can reuse old computations while learning new ones, the model can carry forward the extracted morphological cell features to subsequent Neural Network layers iteratively for elusive learning. The Progressive Resizing technique superimposition in consultation with the Transfer Learning technique while training the Conv Net models has shown a substantial performance increase. The proposed binary and multiclass classification methodology succored in achieving benchmark scores on the Herlev Dataset. We achieved singular multiclass classification scores for WSI images of the SIPaKMed dataset, that is, accuracy (99.70%), precision (99.70%), recall (99.72%), F-Beta (99.63%), and Kappa scores (99.31%), which supersede the scores obtained through principal methodologies. GradCam based feature interpretation extends enhanced assimilation of the generated results, highlighting the pre-malignant and malignant lesions by visual localization in the images.

中文翻译：

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使用带转移学习和渐进式调整大小的convNet在宫颈涂片整个幻灯片图像中检测宫颈癌

宫颈上皮内瘤变（CIN）和宫颈癌是全世界妇女面临的主要健康问题。常规的巴氏涂片（Pap）涂片分析是通过分析拭子图像来诊断宫颈癌前和恶性疾病的有效方法。通过分析巴氏涂片图像，可以探索各种计算机视觉技术来识别潜在的癌前病变和癌变病变。现有的大多数工作涉及使用各种分类器和卷积神经网络进行二进制分类的方法。但是，它们面临着微小特征提取和精确分类的固有挑战。我们提出了一种新颖的方法，可以从全幻灯片图像（WSI）进行宫颈细胞的多类分类，并具有最佳特征提取功能。使用转移学习技术实现Conv网络的实现证实了肿瘤性和肿瘤前病变的有意义的变态诊断。由于渐进式调整大小技术（一种用于训练ConvNet的高级方法）结合了特征层次结构的先验知识，并且可以在学习新计算的同时重用旧的计算，因此该模型可以将提取的形态学细胞特征逐步传递到后续的神经网络层，以实现难以捉摸的学习。在训练Conv Net模型的同时，在与“转移学习”技术协商的情况下，“逐步调整大小”技术的叠加已显示出显着的性能提升。所提出的二进制和多类分类方法成功地在Herlev数据集上获得了基准分数。对于SIPaKMed数据集的WSI图像，我们获得了奇异的多类分类评分，即准确性（99.70％），准确性（99.70％），召回率（99.72％），F贝塔（99.63％）和Kappa评分（99.31％） ，它将取代通过主要方法获得的分数。基于GradCam的特征解释可扩展生成结果的增强同化功能，通过图像中的视觉定位突出显示恶变前和恶性病变。