用YOLOv12模型实现99.3%准确率的白血病细胞早期检测
Early Detection of Acute Myeloid Leukemia (AML) Using YOLOv12 Deep Learning Model
- 基于细胞与核特征，结合色调通道和Otsu阈值预处理图像
- 细胞分割结合Otsu阈值使验证与测试准确率达99.3%
- 适合医学影像分析、病理诊断自动化研究者参考
急性髓系白血病（AML）是最具威胁性的血液癌症之一，其精准分类因各类细胞视觉相似性而极具挑战。本研究利用YOLOv12深度学习模型对多类AML细胞进行分类。采用基于细胞与细胞核特征的两种分割方法，通过色调通道和Otsu阈值技术对图像进行预处理。实验表明，基于细胞分割并结合Otsu阈值的YOLOv12模型在验证集与测试集上均达到99.3%的准确率，表现最优。
原文摘要 · Abstract (English)
Acute Myeloid Leukemia (AML) is one of the most life-threatening type of blood cancers, and its accurate classification is considered and remains a challenging task due to the visual similarity between various cell types. This study addresses the classification of the multiclasses of AML cells Utilizing YOLOv12 deep learning model. We applied two segmentation approaches based on cell and nucleus features, using Hue channel and Otsu thresholding techniques to preprocess the images prior to classification. Our experiments demonstrate that YOLOv12 with Otsu thresholding on cell-based segmentation achieved the highest level of validation and test accuracy, both reaching 99.3%.
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