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具有光谱意识的CNN具有可学习的双直角单位和深度卷曲,用于多类血细胞分类

Sannasi Chakravarthy Sr¹, Harikumar Rajaguru¹, Rajesh Kumar Dhanaraj²

¹Department of Electronics and Communicaiton Engineering, Bannari Amman Institute of Technology, Sathyamangalam 638 401, India.

|November 24, 2025

概括

这项研究引入了一种新的深度学习模型,用于准确的血细胞分类,达到99.18%的准确性. 该模型通过改善特征保留和降低计算成本,提高了白血病和贫血等疾病的早期诊断.

科学领域:

医疗成像医学成像
计算生物学计算生物学
人工智能的人工智能

背景情况:

精确的周围血液细胞分类对于早期诊断白血病和贫血等疾病至关重要.
现有的方法可能面临特征保留和计算效率方面的挑战.

研究的目的:

提出一种新的混合深度学习模型,用于多类血细胞分类.
提高血细胞分类的准确性和效率,以改善疾病诊断.

主要方法:

开发了一种带有光谱意识的卷积神经网络 (CNN) 模型,该模型包含可学习的光谱生物角向下采样单元 (LSBDUs).
替换了传统的聚合层,使用波纹式启发的LSBDUs,以保持卓越的特征.
集成深度可分离卷曲以减少计算开销和培训成本.
利用了8个血细胞类别中的17092张图像的均衡数据集,采用分层数据分割,高级增强和标签光滑.

主要成果:

在血液细胞数据集上实现了99.18%的整体分类准确度.
与现有方法相比,证明了与现有方法相比更优越的类智能的表现.
在所有类别中展示了改进的概括,而没有过度拟合,验证了模型的稳定性.

结论:

拟议的光谱感知CNN模型与LSBDU和深度可分离卷积提供了一个高度准确和高效的解决方案,用于多类血细胞分类.
这一进步为改善临床环境中早期疾病检测和诊断提供了重大潜力.
该模型的设计有效地平衡了功能保存与减少计算复杂性的特征.

关键词:

血细胞分类血细胞分类深度学习是一种深度学习.在深度上可分离的卷积.光谱感知下面的采样波浪式的CNN 波浪式的CNN

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