用SVM增强ResNet-50，在小数据下精准区分红系前体细胞
Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data
- 用SVM作为分类器，结合ResNet-50特征提取，提升小样本识别能力
- 仅用1%数据（每类168张图）时，红系细胞识别精度达98.9%
- 适合医疗资源有限场景，尤其对罕见血细胞分类有实用价值
血液疾病涉及多种影响造血的恶性病与遗传病，临床诊断面临重大挑战，尤其在区分红系前体细胞与白细胞方面。本研究评估了SVM、XG-Boost、KNN和随机森林等机器学习分类器，在不同训练集规模下结合ResNet-50作为主干网络，对血涂片图像中红系前体细胞的检测与分类效果。结果表明，ResNet50-SVM分类器在整体测试准确率与红系前体细胞检测准确率上均优于其他模型，即使在极小数据条件下仍保持高性能。当仅使用完整数据集1%（每类168张图像，共八类）训练时，该方法测试准确率达86.75%，红系前体细胞精确率为98.9%；而未加分类器的预训练ResNet-50模型对应指标为82.03%与98.6%。在数据受限情况下，该方法显著优于传统深度学习模型，为小规模、独特数据集的高精度分类提供了有效解决方案，尤其适用于资源匮乏的医疗环境。
原文摘要 · Abstract (English)
Hematological disorders, which involve a variety of malignant conditions and genetic diseases affecting blood formation, present significant diagnostic challenges. One such major challenge in clinical settings is differentiating Erythroblast from WBCs. Our approach evaluates the efficacy of various machine learning (ML) classifiers$\unicode{x2014}$SVM, XG-Boost, KNN, and Random Forest$\unicode{x2014}$using the ResNet-50 deep learning model as a backbone in detecting and differentiating erythroblast blood smear images across training splits of different sizes. Our findings indicate that the ResNet50-SVM classifier consistently surpasses other models' overall test accuracy and erythroblast detection accuracy, maintaining high performance even with minimal training data. Even when trained on just 1% (168 images per class for eight classes) of the complete dataset, ML classifiers such as SVM achieved a test accuracy of 86.75% and an erythroblast precision of 98.9%, compared to 82.03% and 98.6% of pre-trained ResNet-50 models without any classifiers. When limited data is available, the proposed approach outperforms traditional deep learning models, thereby offering a solution for achieving higher classification accuracy for small and unique datasets, especially in resource-scarce settings.
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