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Abstract：
Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.
摘要：
宫颈癌,影响女性的第二大癌症，由子宫颈细胞异常生长引起，子宫内的重要解剖结构.早期检测的重要性怎么强调都不为过,促使使用各种筛查方法，如巴氏涂片，阴道镜检查,和人乳头瘤病毒（HPV）检测，以确定潜在风险并及时进行干预。这些筛查程序包括目视检查，巴氏涂片检查,阴道镜检查,活检,和HPV-DNA检测,由于癌症诊断固有的主观性质，每个人都需要经验丰富的医生和病理学家的专业知识和技能。为了应对高效智能筛查的当务之急，本文介绍了一种利用预先训练的深度神经网络模型的开创性方法，包括Alexnet,Resnet-101、Resnet-152和InceptionV3，用于特征提取。这些模型的微调伴随着不同机器学习算法的集成，ResNet152展示了卓越的性能，达到98.08%的准确率。值得注意的是，SIPaKMeD数据集，在这项研究中公开访问和利用，有助于我们研究结果的透明度和可重复性。所提出的混合方法结合了DL和ML的各个方面进行宫颈癌分类。图像中最复杂和复杂的特征可以通过DL提取。可以在提取的特征上实现进一步的各种ML算法。这种创新方法不仅有望显着改善宫颈癌检测，而且还强调了智能自动化在医疗诊断领域的变革潜力。为更准确和及时的干预铺平道路。
