用YOLOv8/v11模型实现白血病细胞早期精准识别
Early Diagnosis of Acute Lymphoblastic Leukemia Using YOLOv8 and YOLOv11 Deep Learning Models
- 采用YOLOv8和YOLOv11检测白血病细胞，区分良恶性及不同阶段
- 准确率达98.8%，可有效识别易被误判的造血祖细胞
- 适合医学影像分析、临床辅助诊断场景使用
白血病是严重血液癌症，每年夺走数以千计生命。本研究利用先进图像处理与深度学习技术，聚焦急性淋巴细胞白血病（ALL）的检测。通过人工智能最新进展，评估了YOLOv8和YOLOv11等前沿模型在真实场景中的可靠性。研究重点在于区分恶性与良性白细胞，准确识别ALL的不同阶段，包括早期阶段。同时，模型具备识别造血祖细胞的能力，此类细胞常被误判为白血病。实验结果显示，该方法准确率高达98.8%，展现出在多种数据集和条件下进行稳健、精准白血病检测的巨大潜力。
原文摘要 · Abstract (English)
Leukemia, a severe form of blood cancer, claims thousands of lives each year. This study focuses on the detection of Acute Lymphoblastic Leukemia (ALL) using advanced image processing and deep learning techniques. By leveraging recent advancements in artificial intelligence, the research evaluates the reliability of these methods in practical, real-world scenarios. Specifically, it examines the performance of state-of-the-art YOLO models, including YOLOv8 and YOLOv11, to distinguish between malignant and benign white blood cells and accurately identify different stages of ALL, including early stages. Moreover, the models demonstrate the ability to detect hematogones, which are frequently misclassified as ALL. With accuracy rates reaching 98.8%, this study highlights the potential of these algorithms to provide robust and precise leukemia detection across diverse datasets and conditions.
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