[1]刘乾宇.基于 Faster R-CNN 的疟疾血涂片检测改进算法[J].计算机技术与发展,2021,31(01):61-66.[doi:10. 3969 / j. issn. 1673-629X. 2021. 01. 011]
　LIU Qian-yu.An Improved Algorithm for Malaria Blood Smear Detection Based on Faster R-CNN[J].,2021,31(01):61-66.[doi:10. 3969 / j. issn. 1673-629X. 2021. 01. 011]
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基于 Faster R-CNN 的疟疾血涂片检测改进算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年01期
页码:
61-66
栏目:
图形与图像
出版日期:
2021-01-10

文章信息/Info

Title:
An Improved Algorithm for Malaria Blood Smear Detection Based on Faster R-CNN
文章编号:
1673-629X(2021)01-0061-06
作者:
刘乾宇1; 2
1. 北京邮电大学 软件学院,北京 100876; 2. 北京邮电大学 可信分布式计算与服务教育部重点实验室,北京 100876
Author(s):
LIU Qian-yu1; 2
1. School of Software,Beijing University of Posts and Telecommunications,Beijing 100876,China;
2. Key Laboratory of Trustworthy Distributed Computing and Service (BUPT),Ministry of Education,Beijing 100876,China
关键词:
深度学习; Faster R-CNN; 疟疾; 血涂片; 卷积神经网络; ResNet
Keywords:
deep learning; Faster R-CNN; malaria; blood smear; convolutional neural network; ResNet
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 01. 011
摘要:
根据 WHO 发布的报告,每年疟疾的新发病例超过 2 亿,死亡人数仍居高不下。 疟疾血涂片镜检法是疟疾检测的金标准,但由于人工评估所需的步骤繁琐,即使在经验丰富的医师手中,这种诊断方法也很耗时并且容易发生漏检和误检。 此外疟原虫细胞形状、密度和颜色的变化以及某些细胞类的不确定性等因素,对疟原虫检测提出了重大挑战。 基于深度学习的神经网络模型在对象检测方面取得了巨大成功,但最先进的模型尚未在生物图像数据中得到广泛应用。针对这一问题,提出一种基于深度学习的改进 Faster R-CNN 算法,用来识别疟疾血涂片细胞并检测其受感染的阶段。 在原始Faster R-CNN 的基础上,加入卷积滤波器层,采用提取特征更好的深度残差网络,通过优化锚点的属性以改善疟疾血涂片细胞分类与检测中存在的漏检、误检的问题。 实验结果表明,改进的 Faster R-CNN 模型在公开的间日疟原虫(疟疾)感染人血凃片数据集测试的平均正确率达到了 79.56%,比原始 Faster R-CNN 模型提高了 8.84% 。
Abstract:
According to the WHO,more than 200 million new malaria cases occur each year,and the number of deaths remains high. Malaria blood smear microscopy is the gold standard for malaria detection,but due to the tedious steps required for manual evaluation,it is? ?time consuming and prone to missed and false tests,even by experienced physicians. In addition,factors such as changes in the shape, density,and color of plasmodium cells and the uncertainty of certain cell types pose major challenges for the detection of plasmodium. Neural network models based on deep learning have achi-eved great success in object detection,but the most advanced models have not been widely used in biological image data. Aiming at this problem, an improved Faster R-CNN algorithm based on deep learning is proposed to identify malaria blood smear cells and detect their infected stage. Based on the original Faster R-CNN,a convolution filter layer is added,a deep residual network with better extracted features is used, and the attributes of the anchor point are optimized to improve the problems of missed detection and false detection in the classification and detection of malaria blood smear cells. The experiment shows that the improved Faster R-CNN model has an average accuracy rate of 79.56% on the public blood smear dataset of plasmodium vivax (Malaria) infection,which is 8.84% higher than the original Faster R-CNN model.

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更新日期/Last Update: 2020-01-10