Purpose: Cervical cancer significantly impacts global health, where early detection is piv- otal for improving patient outcomes. This study aims to enhance the accuracy of cervical cancer diagnosis by addressing class imbalance through a novel hybrid deep learning model. Methods: The proposed model, RL-CancerNet, integrates EfficientNetV2 and Vision Transformers (ViTs) within a Reinforcement Learning (RL) framework. EfficientNetV2 extracts local features from cervical cytology images to capture fine-grained details, while ViTs analyze these features to recognize global dependencies across image patches. To address class imbalance, an RL agent dynamically adjusts the focus towards minority classes, thus reducing the common bias towards majority classes in medical image classification. Additionally, a Supporter Module incorporating Conv3D and BiLSTM layers with an attention mechanism enhances contextual learning. Results: RL-CancerNet was evaluated on the benchmark cervical cytology datasets Herlev and SipaKMeD, achieving an exceptional accuracy of 99.7%. This performance surpasses several state-of-the-art models, demonstrating the model’s effectiveness in identifying subtle diagnostic features in complex backgrounds. Conclusions: The integration of CNNs, ViTs, and RL into RL-CancerNet significantly improves the diagnostic accuracy of cervical cancer screenings. This model not only advances the field of automated medical screening but also provides a scalable framework adaptable to other medical imaging tasks, potentially enhancing diagnostic processes across various medical domains.
目的：宫颈癌对全球健康构成重大影响，早期检测是改善患者预后的关键。本研究旨在通过一种新型混合深度学习模型解决类别不平衡问题，从而提高宫颈癌诊断的准确性。方法：所提出的RL-CancerNet模型将EfficientNetV2和视觉变换器（ViTs）整合到强化学习（RL）框架中。EfficientNetV2从宫颈细胞学图像中提取局部特征以捕获细粒度细节，而ViTs分析这些特征以识别图像块之间的全局依赖关系。为解决类别不平衡问题，RL智能体动态调整对少数类别的关注，从而减少医学图像分类中常见的对多数类别的偏向。此外，结合Conv3D和BiLSTM层及注意力机制的辅助模块增强了上下文学习能力。结果：RL-CancerNet在Herlev和SipaKMeD两个基准宫颈细胞学数据集上进行评估，取得了99.7%的优异准确率。该性能超越了多种先进模型，证明了该模型在复杂背景中识别细微诊断特征的有效性。结论：将CNN、ViT和RL整合到RL-CancerNet中显著提高了宫颈癌筛查的诊断准确性。该模型不仅推动了自动化医学筛查领域的发展，还提供了一个可扩展的框架，可适应其他医学影像任务，有望提升多医学领域的诊断流程。
Novelty Classification Model Use in Reinforcement Learning for Cervical Cancer
肿瘤(癌症)患者之家