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

Evrita Lusiana Utari; Latifah Listyalina; Desty Ervira Puspaningtyas

doi:10.22146/ijcn.39560

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

https://doi.org/10.22146/ijcn.39560

Evrita Lusiana Utari⁽¹⁾, Latifah Listyalina⁽²⁾, Desty Ervira Puspaningtyas^(3*)

(1) Program Studi S-1 Teknik Elektro, Fakultas Sains dan Teknologi Universitas Respati Yogyakarta
(2) Program Studi S-1 Teknik Elektro, Fakultas Sains dan Teknologi Universitas Respati Yogyakarta
(3) Program Studi S-1 Ilmu Gizi, Fakultas Ilmu Kesehatan Universitas Respati Yogyakarta
(*) Corresponding Author

Abstract

Application of self-organizing mapping as anemia detection using an image of red blood cells

Background: 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.

Keywords

anemia; artificial neural network; blood image detection; self-organizing mapping

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