Patre Pooja, Verma Dipti
Computer Science and Engineering, Vishwavidyalaya Engineering College Ambikapur, Ambikapur, Chhattisgarh, Ambikapur, India.
University Teaching Department, Chhattisgarh Swami Vivekanand Technical University, Bhilai, India.
Rep Pract Oncol Radiother. 2025 Aug 7;30(3):316-331. doi: 10.5603/rpor.105867. eCollection 2025.
Cervical cancer (CC) is a leading cause of cancer-related deaths worldwide, emphasizing the need for accurate and efficient diagnostic tools. Traditional methods of cervical cell classification are time-consuming and susceptible to human error, highlighting the need for automated solutions.
This study introduces the modified hierarchical deep feature fusion (HDFF) method for cervical cell classification using the SIPaKMeD and Herlev datasets. The novelty of this research lies in the integration of hierarchical deep learning features, which allows for more accurate and robust classification. By enhancing the feature extraction process and combining multiple layers of deep learning models, the Modified HDFF method improves classification performance across various tasks, ranging from binary to multi-class problems.
Our results demonstrate that the Modified HDFF method significantly outperforms existing models. In the 2-class task, it achieves an impressive accuracy of 98.88%, surpassing other approaches such as RF-based hierarchical classification (98.43%). Additionally, it maintains high precision, recall, and F1-scores in multi-class tasks, with 98.8% accuracy in the 3-class problem and 98.5% in the 7-class problem.
Overall, the Modified HDFF method shows great promise as a reliable and efficient diagnostic tool for cervical cancer screening. Its superior accuracy across multiple classification tasks highlights its potential for improving early detection and public health outcomes. Further refinement and expanded training datasets can further enhance its performance, making it an invaluable asset in automated cervical cancer detection.
宫颈癌（CC）是全球癌症相关死亡的主要原因之一，这凸显了对准确且高效的诊断工具的需求。传统的宫颈细胞分类方法耗时且易受人为误差影响，这突出了对自动化解决方案的需求。
本研究介绍了使用SIPaKMeD和Herlev数据集进行宫颈细胞分类的改进型分层深度特征融合（HDFF）方法。这项研究的新颖之处在于分层深度学习特征的整合，这使得分类更加准确和稳健。通过增强特征提取过程并结合深度学习模型的多层结构，改进型HDFF方法在从二分类到多分类等各种任务中提高了分类性能。
我们的结果表明，改进型HDFF方法显著优于现有模型。在二分类任务中，它实现了令人印象深刻的98.88%的准确率，超过了基于随机森林的分层分类等其他方法（98.43%）。此外，在多分类任务中，它保持了高精度、召回率和F1分数，在三分类问题中的准确率为98.8%，在七分类问题中的准确率为98.5%。
总体而言，改进型HDFF方法作为一种可靠且高效的宫颈癌筛查诊断工具显示出巨大潜力。其在多个分类任务中的卓越准确率凸显了其在改善早期检测和公共卫生结果方面的潜力。进一步的优化和扩展训练数据集可以进一步提高其性能，使其成为自动化宫颈癌检测中不可或缺的资产。
