Muksimova Shakhnoza, Umirzakova Sabina, Baltayev Jushkin, Cho Young-Im
Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea.
Department of Information Systems and Technologies, Tashkent State University of Economic, Tashkent 100066, Uzbekistan.
Diagnostics (Basel). 2025 Feb 4;15(3):364. doi: 10.3390/diagnostics15030364.
Reinforcement learning (RL) represents a significant advancement in artificial intelligence (AI), particularly for complex sequential decision-making challenges. Its capability to iteratively refine decisions makes it ideal for applications in medicine, such as the detection of cervical cancer; a major cause of mortality among women globally. The Pap smear test, a crucial diagnostic tool for cervical cancer, benefits from enhancements in AI, facilitating the development of automated diagnostic systems that improve screening effectiveness. This research introduces RL-Cervix.Net, a hybrid model integrating RL with convolutional neural network (CNN) technologies, aimed at elevating the precision and efficiency of cervical cancer screenings. RL-Cervix.Net combines the robust ResNet-50 architecture with a reinforcement learning module tailored for the unique challenges of cytological image analysis. The model was trained and validated using three extensive public datasets to ensure its effectiveness under realistic conditions. A novel application of RL for dynamic feature refinement and adjustment based on reward functions was employed to optimize the detection capabilities of the model. The innovative integration of RL into the CNN framework allowed RL-Cervix.Net to achieve an unprecedented classification accuracy of 99.98% in identifying atypical cells indicative of cervical lesions. The model demonstrated superior accuracy and interpretability compared to existing methods, addressing variability and complexities inherent in cytological images. The RL-Cervix.Net model marks a significant breakthrough in the application of AI for medical diagnostics, particularly in the early detection of cervical cancer. By significantly improving diagnostic accuracy and efficiency, RL-Cervix.Net has the potential to enhance patient outcomes through earlier and more precise identification of the disease, ultimately contributing to reduced mortality rates and improved healthcare delivery.
强化学习（RL）是人工智能（AI）领域的一项重大进展，尤其适用于复杂的序列决策挑战。其迭代优化决策的能力使其非常适合医学应用，例如宫颈癌检测；宫颈癌是全球女性死亡的主要原因。巴氏涂片检查作为宫颈癌的关键诊断工具，受益于人工智能的改进，有助于开发提高筛查效果的自动化诊断系统。本研究引入了RL-Cervix.Net，这是一种将强化学习与卷积神经网络（CNN）技术相结合的混合模型，旨在提高宫颈癌筛查的精度和效率。RL-Cervix.Net将强大的ResNet-50架构与针对细胞学图像分析独特挑战量身定制的强化学习模块相结合。该模型使用三个广泛的公共数据集进行训练和验证，以确保其在实际条件下的有效性。基于奖励函数的强化学习动态特征细化和调整的新应用被用于优化模型的检测能力。强化学习与CNN框架的创新集成使RL-Cervix.Net在识别指示宫颈病变的非典型细胞方面达到了前所未有的99.98%的分类准确率。与现有方法相比，该模型表现出更高的准确性和可解释性，解决了细胞学图像中固有的变异性和复杂性。RL-Cervix.Net模型标志着人工智能在医学诊断应用方面的重大突破，特别是在宫颈癌的早期检测方面。通过显著提高诊断准确性和效率，RL-Cervix.Net有可能通过更早、更精确地识别疾病来改善患者预后，最终有助于降低死亡率和改善医疗服务。
