Zhang Yue, Ning Chunyu, Yang Wenjing
Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
Sci Rep. 2025 Jan 25;15(1):3240. doi: 10.1038/s41598-025-87953-1.
The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network. Firstly, the SE module is embedded in DenseNet121 to increase the model's focus on the nucleus region, which contains important diagnostic information, and reduce the focus on redundant information. Secondly, the sizes of the convolutional kernel and pooling window of the Stem layer are adjusted to adapt to the characteristics of the cervical cell images, so that the model can extract the local detailed information more effectively. Finally, the Atrous Dense Block (ADB) is constructed, and four ADB modules are integrated into DenseNet121 to enable the model to acquire global and local salient feature information. The accuracy of A2SDNet121 for two and seven-classification tasks on the Herlev dataset is 99.75% and 99.14%, respectively. The accuracy for two, three, and five-classification tasks on the SIPaKMeD dataset reaches 99.55%, 99.75% and 99.22%, respectively. Compared with other state-of-the-art algorithms, the A2SDNet121 model performs better in the multi-classification task of cervical cells, which can significantly improve the accuracy and efficiency of cervical cancer screening.
宫颈细胞分类技术能够确定细胞异常程度和病理状况，这有助于医生在早期检测出宫颈癌风险，并提高宫颈癌患者的治愈率和生存率。针对宫颈细胞分类准确性低的问题，提出了一种深度卷积神经网络A2SDNet121。A2SDNet121以DenseNet121作为骨干网络。首先，在DenseNet121中嵌入SE模块，以增强模型对包含重要诊断信息的细胞核区域的关注，并减少对冗余信息的关注。其次，调整Stem层的卷积核大小和池化窗口大小，以适应宫颈细胞图像的特征，使模型能够更有效地提取局部细节信息。最后，构建空洞密集块（ADB），并将四个ADB模块集成到DenseNet121中，使模型能够获取全局和局部显著特征信息。A2SDNet121在Herlev数据集上进行二分类和七分类任务的准确率分别为99.75%和99.14%。在SIPaKMeD数据集上进行二分类、三分类和五分类任务的准确率分别达到99.55%、99.75%和99.22%。与其他现有算法相比，A2SDNet121模型在宫颈细胞多分类任务中表现更优，能够显著提高宫颈癌筛查的准确性和效率。
