Aplikasi self-organizing mapping sebagai alat deteksi anemia pada citra sel darah merah

Evrita Lusiana Utari, Latifah Listyalina, Desty Ervira Puspaningtyas
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Abstract

Application of self-organizing mapping as anemia detection using an image of red blood cellsBackground: Anemia is a nutritional problem characterized by changes in blood cell size, especially in microcytic or macrocytic anemia. Iron deficiency anemia is included in hypochromic microcytic anemia because it has a smaller than normal size red blood cell and has a lower than normal hemoglobin (Hb) arising from reduced supply of iron for erythropoiesis (cell maturation process red blood). Analysis based on red blood cell image is a tool to detect anemia using technology applications. Self-organizing mapping (SOM) is one of the artificial neural networks by dividing the input pattern into several groups, so the network output is in the form of groups that are most similar to the input.Objective: To measure the accuracy of SOM for detecting the size of red blood cells in anemia condition.Methods: The type of research was an observational laboratory. The study was conducted at the Electrobiomedical Laboratory of Universitas Respati Yogyakarta from January to August 2018. The sample consisted of anemia and non-anemia red blood cells which had been tested in a laboratory of 92 blood preparations. Stage of measuring red blood cells consisted of pre-processing (cropping, gray scaling, contrast enhancement, and screening), segmentation, feature extraction, and image identification with SOM. The image identification results were concluded by calculating the accuracy of the anemia detection system based on laboratory examination results.Results: The characteristic that distinguishes anemia and non-anemia was in the size of red blood cells. Anemic red blood cells had different pixel intensities than non-anemic red blood cells. The image of non-anemia red blood cells had a full round or oval image. From as many as 92 detections of blood images, five blood images were not by the target results of laboratory tests. The accuracy achieved by the system was 94.57%.Conclusions: The accuracy value of anemia detection using SOM can be used to identify the type of anemia based on red blood cell size.
自组织映射在使用红细胞图像检测贫血中的应用背景:贫血是一种以血细胞大小变化为特征的营养问题,尤其是在小细胞或大细胞贫血中。缺铁性贫血包括在低色素性微细胞性贫血中,因为它的红细胞比正常大小小,并且由于红细胞生成(红细胞成熟过程)的铁供应减少而导致血红蛋白(Hb)低于正常水平。基于红细胞图像的分析是一种利用技术应用检测贫血的工具。自组织映射(SOM)是一种将输入模式划分为多组的人工神经网络,因此网络输出以与输入最相似的组的形式出现。目的:测定SOM检测贫血状态下红细胞大小的准确性。方法:研究类型为观察性实验室。这项研究于2018年1月至8月在日惹Respati大学的电子生物医学实验室进行。样本由贫血和非贫血红细胞组成,这些红细胞已在92种血液制剂的实验室中进行了测试。红细胞测量阶段包括预处理(裁剪、灰度、对比度增强和筛选)、分割、特征提取和SOM图像识别。根据实验室检查结果,通过计算贫血检测系统的准确性,得出图像识别结果。结果:红细胞大小是区分贫血和非贫血的特征。贫血红细胞与非贫血红细胞具有不同的像素强度。非贫血红细胞的图像具有完整的圆形或椭圆形图像。在多达92次血液图像检测中,有5次血液图像不符合实验室测试的目标结果。该系统的准确率为94.57%。结论:SOM检测贫血的准确率值可用于根据红细胞大小识别贫血类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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