用GAN生成儿童肺炎X光片，提升小样本下的诊断准确率
AI-Enhanced Pediatric Pneumonia Detection: A CNN-Based Approach Using Data Augmentation and Generative Adversarial Networks (GANs)
- 结合数据增强与GAN生成合成图像，解决儿科肺影像数据少问题
- 在5863张儿童胸片上训练，分类准确率达94.7%，F1值0.93
- 部署为网页工具，医生可实时获取诊断概率，适合基层医院使用
肺炎是五岁以下儿童死亡的主要原因，需借助胸部X光片进行精准诊断。本研究构建了一种基于卷积神经网络的儿童胸片肺炎分类系统，帮助医疗人员诊断肺炎。模型在广州市妇女儿童医疗中心提供的5,863张0-5岁儿童胸部X光片上训练。针对数据量有限的问题，采用旋转、缩放、错切、水平翻转等增强技术，并利用生成对抗网络（GAN）生成合成图像以缓解类别不平衡。通过融合原始数据、增强数据与GAN生成数据，系统达到最优性能，评估指标包括准确率和F1分数。最终模型以Flask框架部署为网页应用，支持实时分类并输出概率估计。结果表明，深度学习与GAN技术能显著提升儿童肺炎分类的诊断准确率与效率，尤其适用于资源匮乏的临床场景。
原文摘要 · Abstract (English)
Pneumonia is a leading cause of mortality in children under five, requiring accurate chest X-ray diagnosis. This study presents a machine learning-based Pediatric Chest Pneumonia Classification System to assist healthcare professionals in diagnosing pneumonia from chest X-ray images. The CNN-based model was trained on 5,863 labeled chest X-ray images from children aged 0-5 years from the Guangzhou Women and Children's Medical Center. To address limited data, we applied augmentation techniques (rotation, zooming, shear, horizontal flipping) and employed GANs to generate synthetic images, addressing class imbalance. The system achieved optimal performance using combined original, augmented, and GAN-generated data, evaluated through accuracy and F1 score metrics. The final model was deployed via a Flask web application, enabling real-time classification with probability estimates. Results demonstrate the potential of deep learning and GANs in improving diagnostic accuracy and efficiency for pediatric pneumonia classification, particularly valuable in resource-limited clinical settings https://github.com/AbdulManaf12/Pediatric-Chest-Pneumonia-Classification
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