用手机拍眼睑和指甲，自动判断是否贫血，准确率超96%。
AnemiaVision: Non-Invasive Anemia Detection via Smartphone Imagery Using EfficientNet-B3 with TrivialAugmentWide, Mixup Augmentation, and Persistent Patient History Management
- 用EfficientNet-B3模型结合多种增强技术，提升图像识别精度。
- 在真实数据上达到96.2%准确率，比基线高50%以上。
- 适合农村医疗人员用手机做快速筛查，代码开源可直接使用。
贫血影响全球超过十亿人，但在资源匮乏地区因缺乏实验室检测而严重漏诊。本文提出AnemiaVision，一个基于网页的无创贫血筛查系统，通过智能手机拍摄的眼睑结膜和指甲床图像进行诊断。该系统微调预训练的EfficientNet-B3主干网络，采用三层重构分类头，包含BatchNorm、GELU激活和高丢弃率（0.45/0.35）。训练中引入四种增效技术：TrivialAugmentWide（无需策略的图像增强）、RandomErasing（空间正则化）、Mixup（alpha=0.2，跨类别平滑）及余弦退火调度与线性预热。采用以峰值验证准确率为依据的早停策略，避免高方差周期下的过早终止。部署的Flask应用结合PostgreSQL数据库（运行于Render），通过自动化迁移入口保障重部署零数据丢失。消融实验显示，以准确率为导向的早停提升1.6%，Mixup提升2.8%。整体系统验证准确率达96.2%，AUC-ROC为0.98，相较三轮仅限CPU的基线（准确率44.9%，AUC-ROC 0.58）显著提升。贫血类敏感度达0.96，适合作为乡村基层医疗人员的一线筛查工具。系统已公开可用，源码开源。
原文摘要 · Abstract (English)
Anemia affects over one billion people globally and remains severely under-diagnosed in low-resource regions where laboratory blood tests are inaccessible. This paper presents AnemiaVision, an end-to-end web-based system for non-invasive anemia screening from smartphone photographs of the palpebral conjunctiva and fingernail beds. The proposed pipeline fine-tunes a pre-trained EfficientNet-B3 backbone with a redesigned three-layer classifier head incorporating BatchNorm, GELU activations, and high-rate Dropout (0.45/0.35). Training employs four orthogonal accuracy-boosting techniques: TrivialAugmentWide for policy-free image augmentation, RandomErasing for spatial regularisation, Mixup (alpha=0.2) for inter-class smoothing, and cosine-annealing scheduling with linear warmup. Early stopping is governed by peak validation accuracy rather than validation loss to prevent premature termination on high-variance epochs. The deployed Flask application integrates persistent patient-history management backed by PostgreSQL on Render, with an automated database-migration entrypoint ensuring zero data loss across redeploys. Ablation experiments demonstrate that accuracy-first early stopping contributes +1.6% and Mixup contributes +2.8% to final validation accuracy. Overall, the proposed system achieves a validation accuracy of 96.2% and AUC-ROC of 0.98, compared with 44.9% validation accuracy and AUC-ROC of 0.58 from the three-epoch CPU-only baseline. Sensitivity for the anemic class reaches 0.96, making the system suitable as a first-line screening tool for community health workers in rural settings. The system is publicly accessible and source code is openly available.
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