Healthy and unhealthy red blood cell detection in human blood smears using neural networks
Healthy and unhealthy red blood cell detection in human blood smears using neural networks
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DOI：
10.1016/j.micron.2016.01.008
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发表时间：
2016-04-01
期刊：
影响因子：
2.4
通讯作者：
Elsalamony, Hany A.
中科院分区：
文献类型：
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作者：
Elsalamony, Hany A.
影响人类红细胞（RBC）的最常见疾病之一是贫血。为了诊断贫血，通常采用以下方法：基于测量血红蛋白水平的鉴定过程和基于血液涂片中显微镜检查的RBC分类。本文提出了一种建议的算法检测和计数三种类型的贫血感染的红细胞在显微彩色图像使用圆形霍夫变换和形态学工具。贫血细胞包括镰状细胞、椭圆形红细胞、微小细胞和形状不明的细胞。此外，从检测过程中得到的数据已经通过一种流行的数据分析技术进行了分析：神经网络。该模型的实验结果证明了分析健康/不健康细胞的高准确性。该算法在21个显微图像中实现了约97.8%的所有细胞的最大检测。使用神经网络分别对镰状细胞、椭圆形红细胞、微小细胞和形状未知的细胞实现了100%、98%、100%和99.3%的有效率。(C)2016由Elsevier Ltd.出版
One of the most common diseases that affect human red blood cells (RBCs) is anaemia. To diagnose anaemia, the following methods are typically employed: an identification process that is based on measuring the level of haemoglobin and the classification of RBCs based on a microscopic examination in blood smears. This paper presents a proposed algorithm for detecting and counting three types of anaemia-infected red blood cells in a microscopic coloured image using circular Hough transform and morphological tools. Anaemia cells include sickle, elliptocytosis, microsite cells and cells with unknown shapes. Additionally, the resulting data from the detection process have been analysed by a prevalent data analysis technique: the neural network. The experimental results for this model have demonstrated high accuracy for analysing healthy/unhealthy cells. This algorithm has achieved a maximum detection of approximately 97.8% of all cells in 21 microscopic images. Effectiveness rates of 100%, 98%, 100%, and 99.3% have been achieved using neural networks for sickle cells, elliptocytosis cells, microsite cells and cells with unknown shapes, respectively. (C) 2016 Published by Elsevier Ltd.