DeepCervix：一个基于深度学习的框架，用于使用混合深度特征融合技术对宫颈细胞进行分类
DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques
摘要：
子宫颈癌是妇女最常见的致命癌症之一，可以通过定期检查来预防，以在早期阶段发现任何癌前病变并进行治疗。巴氏涂片检查是一种广泛使用的早期发现宫颈癌的筛查技术，而这种人工筛查方法由于人为错误而具有很高的假阳性结果。为了改进手动筛查实践，已经广泛研究了基于机器学习（ML）和深度学习（DL）的计算机辅助诊断（CAD）系统以分类宫颈巴氏细胞。现有的大多数研究需要预分割的图像，以获得良好的分类结果，而准确的宫颈细胞分割是具有挑战性的，因为细胞聚类。一些研究依赖于手工特征，这不能保证分类阶段的最优性。此外，当存在不均匀的数据分布时，DL为多类分类任务提供了较差的性能，这在宫颈细胞数据集中很普遍。这项研究通过提出DeepCerebral（一种基于DL的混合深度特征融合（HDFF）技术）来解决这些局限性，以准确地对宫颈细胞进行分类。我们提出的方法使用各种DL模型来捕获更多潜在的信息，以提高分类性能。我们提出的HDFF方法在公开的SIPAKMED数据集上进行了测试，并与基础DL模型和LF方法进行了性能比较。对于SIPAKMED数据集，我们已经获得了99.85%，99.38%和99.14%的2类，3类和5类分类的最先进的分类准确率。此外，我们的方法进行了测试，在Herlev数据集上，实现了98.32%的二进制类和90.32%的7类分类的准确率。
英文摘要：
Cervical cancer, one of the most common fatal cancers among women, can be prevented by regular screening to detect any precancerous lesions at early stages and treat them. Pap smear test is a widely performed screening technique for early detection of cervical cancer, whereas this manual screening method suffers from high false-positive results because of human errors. To improve the manual screening practice, machine learning (ML) and deep learning (DL) based computer-aided diagnostic (CAD) systems have been investigated widely to classify cervical pap cells. Most of the existing researches require pre-segmented images to obtain good classification results, whereas accurate cervical cell segmentation is challenging because of cell clustering. Some studies rely on handcrafted features, which cannot guarantee the classification stage's optimality. Moreover, DL provides poor performance for a multiclass classification task when there is an uneven distribution of data, which is prevalent in the cervical cell dataset. This investigation has addressed those limitations by proposing DeepCervix, a hybrid deep feature fusion (HDFF) technique based on DL to classify the cervical cells accurately. Our proposed method uses various DL models to capture more potential information to enhance classification performance. Our proposed HDFF method is tested on the publicly available SIPAKMED dataset and compared the performance with base DL models and the LF method. For the SIPAKMED dataset, we have obtained the state-of-the-art classification accuracy of 99.85%, 99.38%, and 99.14% for 2-class, 3-class, and 5-class classification. Moreover, our method is tested on the Herlev dataset and achieves an accuracy of 98.32% for binary class and 90.32% for 7-class classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
