基于定制CNN模型的早产儿视网膜病变高效自动化诊断
Efficient Automated Diagnosis of Retinopathy of Prematurity by Customize CNN Models
AI总结：
针对早产儿视网膜病变（ROP）自动化诊断需求，提出定制CNN模型结合投票机制的方案，实验证明其诊断精度、效率优于预训练模型，计算负担更低，可部署于临床辅助诊断场景。
摘要：
本文采用先进深度学习方法对早产儿视网膜病变（Retinopathy of Prematurity, ROP）诊断开展了深入研究，重点优化并评估基于卷积神经网络（CNN）的方案以实现精准高效的ROP检测。研究围绕数据集整理、预处理策略与模型架构设计展开，研究目标涵盖模型有效性、计算成本分析与时间复杂度评估。实验结果表明，定制CNN模型在准确率与F1分数上均优于预训练模型，引入投票机制后性能进一步提升。此外，本研究提出的定制CNN模型可缓解深度神经网络带来的计算负担，同时验证了该类模型在特定软硬件配置下部署的可行性，凸显其作为临床场景中有效诊断辅助工具的价值。综上，本研究为ROP诊断领域做出了重要贡献，证实了深度学习模型在提升诊断精度与效率方面的有效性。
英文摘要：
This paper encompasses an in-depth examination of Retinopathy of Prematurity (ROP) diagnosis, employing advanced deep learning methodologies. Our focus centers on refining and evaluating CNN-based approaches for precise and efficient ROP detection. We navigate the complexities of dataset curation, preprocessing strategies, and model architecture, aligning with research objectives encompassing model effectiveness, computational cost analysis, and time complexity assessment. Results underscore the supremacy of tailored CNN models over pre-trained counterparts, evident in heightened accuracy and F1-scores. Implementation of a voting system further enhances performance. Additionally, our study reveals the potential of the proposed customized CNN model to alleviate computational burdens associated with deep neural networks. Furthermore, we showcase the feasibility of deploying these models within dedicated software and hardware configurations, highlighting their utility as valuable diagnostic aids in clinical settings. In summary, our discourse significantly contributes to ROP diagnosis, unveiling the efficacy of deep learning models in enhancing diagnostic precision and efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
