标题： 基于CNN的结膜苍白贫血检测的训练后量化性能分析 显示英文标题

标题： Performance Analysis of Post-Training Quantization for CNN-based Conjunctival Pallor Anemia Detection

摘要： 贫血是一种全球范围内的常见健康问题，尤其是在低资源环境中的幼儿中更为普遍。 传统的贫血检测方法通常需要昂贵的设备和专业知识，这给早期和准确的诊断带来了障碍。 为了解决这些挑战，我们探索了通过结膜苍白来检测贫血的深度学习模型，重点研究了CP-AnemiC数据集，该数据集包含710张6至59个月大儿童的图像。 该数据集标注了血红蛋白水平、性别、年龄和其他人口统计数据，使得能够开发出用于准确检测贫血的机器学习模型。 我们使用MobileNet架构作为主干网络，因其在移动和嵌入式视觉应用中的高效性，并通过数据增强技术和交叉验证策略对模型进行端到端微调。 我们的模型实现达到了0.9313的准确率、0.9374的精确率和0.9773的F1分数，显示出在数据集上的强大性能。 为了优化模型以便在边缘设备上部署，我们进行了训练后量化，评估了不同位宽（FP32、FP16、INT8和INT4）对模型性能的影响。 初步结果表明，虽然FP16量化保持了较高的准确率（0.9250）、精确率（0.9370）和F1分数（0.9377），但更激进的量化（INT8和INT4）会导致性能显著下降。 总体而言，我们的研究支持进一步探索量化方案和硬件优化，以评估移动医疗应用中模型大小、推理时间和诊断准确性之间的权衡。 显示英文摘要

摘要： Anemia is a widespread global health issue, particularly among young children in low-resource settings. Traditional methods for anemia detection often require expensive equipment and expert knowledge, creating barriers to early and accurate diagnosis. To address these challenges, we explore the use of deep learning models for detecting anemia through conjunctival pallor, focusing on the CP-AnemiC dataset, which includes 710 images from children aged 6-59 months. The dataset is annotated with hemoglobin levels, gender, age and other demographic data, enabling the development of machine learning models for accurate anemia detection. We use the MobileNet architecture as a backbone, known for its efficiency in mobile and embedded vision applications, and fine-tune our model end-to-end using data augmentation techniques and a cross-validation strategy. Our model implementation achieved an accuracy of 0.9313, a precision of 0.9374, and an F1 score of 0.9773 demonstrating strong performance on the dataset. To optimize the model for deployment on edge devices, we performed post-training quantization, evaluating the impact of different bit-widths (FP32, FP16, INT8, and INT4) on model performance. Preliminary results suggest that while FP16 quantization maintains high accuracy (0.9250), precision (0.9370), and F1 Score (0.9377), more aggressive quantization (INT8 and INT4) leads to significant performance degradation. Overall, our study supports further exploration of quantization schemes and hardware optimizations to assess trade-offs between model size, inference time, and diagnostic accuracy in mobile healthcare applications.