Automatic Recognition of Blood Cell Images with Dense Distributions Based on a Faster Region-Based Convolutional Neural Network
Source abstract
Abstract
In modern clinical medicine, the important information of red blood cells, such as shape and number, is applied to detect blood diseases. However, the automatic recognition problem of single cells and adherent cells always exists in a densely distributed medical scene, which is difficult to solve for both the traditional detection algorithms with lower recognition rates and the conventional networks with weaker feature extraction capabilities. In this paper, an automatic recognition method of adherent blood cells with dense distribution is proposed. Based on the Faster R-CNN, the balanced feature pyramid structure, deformable convolution network, and efficient pyramid split attention mechanism are adopted to automatically recognize the blood cells under the conditions of dense distribution, extrusion deformation, adhesion and overlap. In addition, the Align algorithm for region of interest also contributes to improving the accuracy of recognition results. The experimental results show that the mean average precision of cell detection is 0.895, which is 24.5% higher than that of the original network model. Compared with the one-stage mainstream networks, the presented network has a stronger feature extraction capability. The proposed method is suitable for identifying single cells and adherent cells with dense distribution in the actual medical scene.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Imaging for Blood Diseases · AI in cancer detection · COVID-19 diagnosis using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Funding
National Natural Science Foundation of China · Key Research and Development Plan Project in Shaanxi Province · Natural Science Basic Research Program of Shaanxi · Xi’an Science and Technology Plan Project · Seed Fund for Creativity and Innovation of Postgraduates of Xi’an University of Technology · 2023 Training Program of Innovation and Entrepreneurship for Undergraduates
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