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使用深度转移学习和活跃轮进行爆细胞的自动分类和细分

Divine Senanu Ametefe¹, Suzi Seroja Sarnin¹, Darmawaty Mohd Ali¹

¹Wireless Communication Technology Group, College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia.

International journal of laboratory hematology

|May 10, 2024

概括

这项研究介绍了ALLDet,一种使用EfficientNetB3的AI工具,用于从细胞图像中准确检测急性淋巴细胞白血病 (ALL). 它显著改进了手工方法,用于更快,更可靠的诊断.

科学领域:

人工智能在医学中的应用
计算血液学计算血液学
医学诊断医学诊断医学诊断

背景情况:

急性淋巴细胞白血病 (ALL) 诊断依赖于手动显微镜,这是劳动密集型的,容易出现人为错误.
将白血病细胞与正常细胞区分开来需要专门的专业知识,并且可能具有挑战性.

研究的目的:

开发一个自动化系统,使用深度学习来准确检测ALL.
通过克服手工方法的局限性,提高白血病诊断的精度.

主要方法:

使用深度转移学习与9个CNN模型,包括EfficientNetB3,用于ALL分类.
采用基于Chan-Vese模型的细分来精确隔离白细胞 (WBC) 核.

主要成果:

EfficientNetB3实现了高性能,98.5%的回忆特异性,95.86%的精度和97.13%的整体准确性.
陈维斯细分有效处理不规则的爆细胞形状和噪声,这对于准确分析至关重要.

结论:

使用EfficientNetB3和高级细分的ALLDet分类器代表了ALL检测的重大进步.
这种人工智能驱动的方法有望通过及时和精确的诊断来改善患者护理.
这项研究为进一步将AI整合到医学诊断领域铺平了道路.

关键词:

陈维斯的模型模型活动轮活动轮.急性淋巴细胞白血病 (ALL)爆炸细胞的爆炸细胞.深度转移学习是指深度转移学习.

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