摘要
当前在处理急性淋巴细胞白血病(acute lymphoblastic leukemia,ALL)分类时存在着背景信息杂乱和的差异性细微问题.由于在血液样本图像中,选取关键特征并减少背景噪声仍然困难,传统方法难以捕捉到重要且细微的特征,难以有效地分类和识别各种血液细胞类型,进而影响了结果的准确性与可靠性.本文提出一种基于ResNeXt50分类模型,采用图像增强来减少背景噪声对图像的影响,并通过改进空洞金字塔特征提取方法增强对各个尺度和上下文信息的感知能力,加入改进SA注意力机制,使得模型可以更好地关注并学习对结果影响较大的信息.本文提出的模型在伊朗德黑兰(Taleqani)医院的Blood Cells Cancer公开数据集进行了实验,准确率和精确率分别达到了98.39%,98.33%,结果表明该模型不仅具备一定的临床意义和实用价值,而且为ALL辅助诊断提供了新的思路.
Currently,when dealing with the classification of acute lymphoblastic leukemia(ALL),there are problems of messy background information and nuanced differences.Since it is still difficult to select key features and reduce background noise in blood sample images,it is difficult for traditional methods to capture important and subtle features,and effectively classify and identify various blood cell types,which affects the accuracy and reliability of the results.This study proposes a classification model based on ResNeXt50,which uses image enhancement to reduce background noise.The model enhances the perception of various scales and context information by improving the hole pyramid feature extraction method.By adding an improved SA attention mechanism,the model can better focus on and learn information that has a greater impact on the outcome.The model is tested on the Blood Cells Cancer public data set of Tehran(Taleqani) Hospital in Iran,and the accuracy and precision rates reach 98.39% and 98.33%,respectively.The results show that the model not only has certain clinical significance and practical value but also provides a new idea for the auxiliary diagnosis of ALL.
作者
邵宇飞
于潍赫
刘阳
SHAO Yu-Fei;YU Wei-He;LIU Yang(Software College,Liaoning Technical University,Huludao 125105,China;School of Software,Shenyang University of Technology,Shenyang 110870,China)
出处
《计算机系统应用》
2024年第6期117-125,共9页
Computer Systems & Applications
基金
中央高校基本科研业务费(N2202011)。
