Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA.
Comput Biol Med. 2021 Sep;136:104649. doi: 10.1016/j.compbiomed.2021.104649. Epub 2021 Jul 20.
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 studies require pre-segmented images to obtain good classification results. In contrast, 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 late fusion (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. This method is also tested on the Herlev dataset and achieves an accuracy of 98.32% for 2-class and 90.32% for 7-class classification. The source code of the DeepCervix model is available at: https://github.com/Mamunur-20/DeepCervix.
宫颈癌是女性中最常见的致命癌症之一，可以通过定期筛查来预防，以在早期发现任何癌前病变并进行治疗。巴氏涂片检查是一种广泛应用的筛查技术，用于早期发现宫颈癌，但这种手动筛查方法由于人为错误而导致高假阳性结果。为了改进手动筛查实践，基于机器学习 (ML) 和深度学习 (DL) 的计算机辅助诊断 (CAD) 系统已被广泛研究，以对宫颈巴氏细胞进行分类。大多数现有研究都需要预分割的图像来获得良好的分类结果。相比之下，由于细胞聚类，准确的宫颈细胞分割具有挑战性。一些研究依赖于手工制作的特征，这不能保证分类阶段的最优性。此外，当数据分布不均匀时，DL 在多类分类任务中表现不佳，而这种情况在宫颈细胞数据集中很常见。通过提出 DeepCervix，一种基于 DL 的混合深度特征融合 (HDFF) 技术，本研究解决了这些限制，以准确分类宫颈细胞。我们提出的方法使用各种 DL 模型来捕获更多潜在信息，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 模型和后期融合 (LF) 方法进行了性能比较。对于 SIPaKMeD 数据集，我们获得了 99.85%、99.38%和 99.14%的 2 类、3 类和 5 类分类的最新分类精度。该方法还在 Herlev 数据集上进行了测试，在 2 类分类中达到了 98.32%的准确率，在 7 类分类中达到了 90.32%的准确率。DeepCervix 模型的源代码可在：https://github.com/Mamunur-20/DeepCervix。
