标题： 基于深度学习的胸部X光图像肺炎检测：一种具有性能分析和临床意义的CNN方法 显示英文标题

标题： Deep Learning-Based Pneumonia Detection from Chest X-ray Images: A CNN Approach with Performance Analysis and Clinical Implications

摘要： 深度学习整合到医学影像系统中，通过专注于肺炎识别，已改变了疾病检测和诊断过程。 该研究介绍了一个复杂的深度学习系统，使用卷积神经网络从胸部X光图像中自动检测肺炎，从而提高了诊断的精确度和速度。 所提出的CNN架构集成了包括可分离卷积、批量归一化和dropout正则化在内的复杂方法，以增强特征提取并减少过拟合。 通过应用数据增强技术和自适应学习率策略，该模型在大量胸部X光图像上进行了训练，以提高其泛化能力。 评估指标的复杂数组，如准确率、精确率、召回率和F1分数，共同验证了模型的卓越性能，记录的准确率为91。 该研究不仅关注模型性能，还解决了关键的临床实施障碍，如数据隐私保护、模型可解释性和与现有医疗系统的集成。 这种方法通过将医学本体与语义技术相结合，引入了一项重要进展，以提高诊断准确性。 该研究通过将机器学习输出与结构化的医学知识框架相结合，增强了AI诊断的可靠性，以提高可解释性。 研究结果表明，基于人工智能的医疗工具是一种可扩展、高效的肺炎检测解决方案。 该研究通过开发更精确的自动化诊断方法，推进了人工智能在临床环境中的整合，这些方法能够提供一致的医学影像结果。 显示英文摘要

摘要： Deep learning integration into medical imaging systems has transformed disease detection and diagnosis processes with a focus on pneumonia identification. The study introduces an intricate deep learning system using Convolutional Neural Networks for automated pneumonia detection from chest Xray images which boosts diagnostic precision and speed. The proposed CNN architecture integrates sophisticated methods including separable convolutions along with batch normalization and dropout regularization to enhance feature extraction while reducing overfitting. Through the application of data augmentation techniques and adaptive learning rate strategies the model underwent training on an extensive collection of chest Xray images to enhance its generalization capabilities. A convoluted array of evaluation metrics such as accuracy, precision, recall, and F1 score collectively verify the model exceptional performance by recording an accuracy rate of 91. This study tackles critical clinical implementation obstacles such as data privacy protection, model interpretability, and integration with current healthcare systems beyond just model performance. This approach introduces a critical advancement by integrating medical ontologies with semantic technology to improve diagnostic accuracy. The study enhances AI diagnostic reliability by integrating machine learning outputs with structured medical knowledge frameworks to boost interpretability. The findings demonstrate AI powered healthcare tools as a scalable efficient pneumonia detection solution. This study advances AI integration into clinical settings by developing more precise automated diagnostic methods that deliver consistent medical imaging results.