YOLOv10在血细胞检测中表现优异，提升诊断准确率。
Transforming Blood Cell Detection and Classification with Advanced Deep Learning Models: A Comparative Study
- 采用YOLOv10模型，图像尺寸640x640，训练多轮提升性能
- 训练轮次越多，准确率、精确率与召回率越高，尤其在实时检测中
- 新标注数据集开源，适合医疗影像与深度学习研究者
高效检测与分类血细胞对准确诊断和有效治疗血液疾病至关重要。本研究使用在Roboflow数据集上训练的YOLOv10模型，图像统一缩放至640x640像素，通过不同训练轮次进行评估。结果表明，增加训练轮次显著提升了准确率、精确率与召回率，尤其在实时血细胞检测与分类中表现突出。尽管MobileNetV2和ShuffleNetV2计算效率更高，DarkNet在特征提取方面更优，但YOLOv10在实时性能上优于三者。研究强调将YOLOv10、MobileNetV2、ShuffleNetV2与DarkNet等深度学习模型融入临床流程的潜力，有望提升诊断准确性和效率。此外，本文构建了一个新且标注良好的血细胞数据集，将公开共享，以推动自动血细胞检测与分类的进一步发展。研究结果展示了这些模型在革新医学诊断与优化血液病管理中的变革性影响。
原文摘要 · Abstract (English)
Efficient detection and classification of blood cells are vital for accurate diagnosis and effective treatment of blood disorders. This study utilizes a YOLOv10 model trained on Roboflow data with images resized to 640x640 pixels across varying epochs. The results show that increased training epochs significantly enhance accuracy, precision, and recall, particularly in real-time blood cell detection & classification. The YOLOv10 model outperforms MobileNetV2, ShuffleNetV2, and DarkNet in real-time performance, though MobileNetV2 and ShuffleNetV2 are more computationally efficient, and DarkNet excels in feature extraction for blood cell classification. This research highlights the potential of integrating deep learning models like YOLOv10, MobileNetV2, ShuffleNetV2, and DarkNet into clinical workflows, promising improvements in diagnostic accuracy and efficiency. Additionally, a new, well-annotated blood cell dataset was created and will be open-sourced to support further advancements in automatic blood cell detection and classification. The findings demonstrate the transformative impact of these models in revolutionizing medical diagnostics and enhancing blood disorder management
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