用轻量CNN自动识别胸片肺炎，提升诊断效率与准确性。
Deep Learning Based CNN Model for Automated Detection of Pneumonia from Chest XRay Images
- 设计专用深度可分离卷积网络，适配灰度医学影像纹理特征。
- 在5863张胸片上实现高精度检测，有效缓解类别不平衡问题。
- 适合医疗资源不足地区，助力基层快速筛查肺炎。
肺炎是全球范围内导致发病率和死亡率的主要疾病之一，尤其在儿童和老年人群中更为突出，且在资源匮乏地区尤为严重。快速准确的诊断对临床干预至关重要，但传统依赖人工判读胸片的方法常受观察者差异、疲劳及合格放射科医生短缺的影响。为此，本文提出一种基于自定义卷积神经网络（CNN）的统一自动化诊断模型，可高效精准识别胸片中的肺炎，且计算开销极低。与多数采用通用迁移学习的模型不同，该架构采用定制化的深度可分离卷积设计，针对灰度医学图像的纹理特性进行优化。同时结合对比限制自适应直方图均衡化（CLAHE）与几何增强技术，有效缓解类别不平衡，提升泛化能力。系统在包含5863张前后位胸片的数据集上进行测试，验证了其卓越性能。
原文摘要 · Abstract (English)
Pneumonia has been one of the major causes of morbidities and mortality in the world and the prevalence of this disease is disproportionately high among the pediatric and elderly populations especially in resources trained areas Fast and precise diagnosis is a prerequisite for successful clinical intervention but due to inter observer variation fatigue among experts and a shortage of qualified radiologists traditional approaches that rely on manual interpretation of chest radiographs are frequently constrained To address these problems this paper introduces a unified automated diagnostic model using a custom Convolutional Neural Network CNN that can recognize pneumonia in chest Xray images with high precision and at minimal computational expense In contrast like other generic transfer learning based models which often possess redundant parameters the offered architecture uses a tailor made depth wise separable convolutional design which is optimized towards textural characteristics of grayscale medical images Contrast Limited Adaptive Histogram Equalization CLAHE and geometric augmentation are two significant preprocessing techniques used to ensure that the system does not experience class imbalance and is more likely to generalize The system is tested using a dataset of 5863 anterior posterior chest Xrays.
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