Red Blood Cell Detection Using Improved Mask R-CNN

被引：0

作者：

Pan, Hongfang ^{[1
]}

Su, Han ^{[1
]}

Chen, Jin ^{[1
]}

Tong, Ying ^{[1
]}

机构：

[1] Tianjin Normal Univ, Tianjin Key Lab Wireless Mobile Commun & Power Tr, Tianjin 300387, Peoples R China

来源：

ADVANCES IN BRAIN INSPIRED COGNITIVE SYSTEMS, BICS 2023 | 2024年 / 14374卷

关键词：

Deep Learning; Red blood cell detection; Mask R-CNN; Split-attention network;

D O I：

10.1007/978-981-97-1417-9_10

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Red blood cell detection is an important example of this task. Manual detection is not only labor-intensive, but also prone to misdirection and omission. In order to enhance the speed and accuracy, an improved mask regional convolution neural network (Mask R-CNN) is proposed in this paper. The algorithm utilizes Split-Attention Networks (ResNeSt) as a feature extraction network. ResNeSt combines channel attention with multi-path representation, and feature extraction is performed in combination with Feature Pyramid Network (FPN). The experimental results show that the improved Mask R-CNN has an average precision increase of 2.55%, and improves the efficiency of red blood cell detection.

引用

页码：105 / 112

页数：8

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