用深度学习精准识别白血病细胞，准确率达99.7%。
Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models
- 结合YOLOv8/v11与ResNet50等模型，自动区分癌变与正常白细胞。
- 在多数据集上实现99.7%准确率，可识别早期白血病及易混淆的血细胞。
- 适合医学影像分析、智能诊断系统研发人员参考。
每年有大量患者死于白血病。随着人工智能技术发展，其在医疗场景中的适用性与可靠性仍待验证。本研究采用图像处理与深度学习方法，基于真实世界数据，实现急性淋巴细胞白血病（ALL）的先进检测。ALL是一种侵袭性白血病，本文评估了最新YOLO系列模型及其性能，解决白细胞良恶性判别的关键问题，并可识别不同分期，包括早期阶段。同时，模型能准确区分常被误诊为ALL的造血祖细胞。通过YOLOv8、YOLOv11、ResNet50和Inception-ResNet-v2等先进模型，在多个数据集上达到最高99.7%的准确率，证明其在多种实际场景下的有效性。
原文摘要 · Abstract (English)
Thousands of individuals succumb annually to leukemia alone. As artificial intelligence-driven technologies continue to evolve and advance, the question of their applicability and reliability remains unresolved. This study aims to utilize image processing and deep learning methodologies to achieve state-of-the-art results for the detection of Acute Lymphoblastic Leukemia (ALL) using data that best represents real-world scenarios. ALL is one of several types of blood cancer, and it is an aggressive form of leukemia. In this investigation, we examine the most recent advancements in ALL detection, as well as the latest iteration of the YOLO series and its performance. We address the question of whether white blood cells are malignant or benign. Additionally, the proposed models can identify different ALL stages, including early stages. Furthermore, these models can detect hematogones despite their frequent misclassification as ALL. By utilizing advanced deep learning models, namely, YOLOv8, YOLOv11, ResNet50 and Inception-ResNet-v2, the study achieves accuracy rates as high as 99.7%, demonstrating the effectiveness of these algorithms across multiple datasets and various real-world situations.
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