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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References

  1. U.S. Department of Health and Human Services. Your guide to anemia: anemia. [series online] 2011 [cited 2018 Juli]. Available from: URL: https://www.nhlbi.nih.gov/files/docs/public/blood/anemia-yg.pdf
  2. Masrizal. Studi literatur: anemia defisiensi besi. Jurnal Kesehatan Masyarakat. 2007;II(1):140-5.
  3. Badan Penelitian dan Pengembangan Kesehatan, Kementerian Kesehatan RI. Riset Kesehatan Dasar: Riskesdas 2013. Jakarta: Kementerian Kesehatan RI; 2013.
  4. Rusmawatiningtyas D, Setyowireni D, Mulatsih S, Sutaryo. Early detection of anemia among school children using the World Health Organization Hemoglobin Color Scale 2006. Paediatrica Indonesiana. 2009;49(3):135-8. doi: 10.14238/pi49.3.2009.135-8
  5. Rahma ON, Saraswati SA, Suharaningsih. Implementasi jaringan saraf tiruan sebagai alat bantu identifikasi anemia pada citra sel darah merah. [series online] 2013 [cited 2018 Juli]. Available from: URL: https://www.researchgate.net/publication/264119971
  6. Listyalina L. Identifikasi otomatis anemia pada citra sel darah merah berbasis komputer. Electrician: Jurnal Rekayasa dan Teknologi Elektro. 2017;11(3):92-8. doi: 10.23960/elc.v11n3.2057
  7. Setiawan A, Suryani E, Wiharto. Segmentasi citra sel darah merah berdasarkan morfologi sel untuk mendeteksi anemia defisiensi besi. ITSMART: Jurnal Teknologi dan Informasi. 2014;3(1):1-8. doi: 10.20961/itsmart.v3i1.638
  8. Hartadi D, Sumardi RRI. Simulasi perhitungan sel darah merah. Transmisi. 2004; 8(2):1-6.
  9. Aprilianti LM, Usman K, Nugroho H. Perhitungan sel darah merah berbasis pengolahan citra digital. Prosiding Seminar Nasional IV UTY; 2008; Yogyakarta.
  10. Riyanti ME. Deteksi dan klasifikasi penyakit anemia (defisiensi besi, hemolitik dan hemoglobinopati) berdasarkan struktur fisis sel darah merah menggunakan pengolahan citra digital [Skripsi]. Bandung: Institut Teknologi Telkom; 2009.
  11. Lee H, Chen Y-PP. Cell morphology based classification for red cells in blood smear images. Pattern Recogn Lett. 2014;49:155-61. doi: 10.1016/j.patrec.2014.06.010
  12. Leleury ZA, Patty HWM. Analisis cluster dan diagnosa penyakit menggunakan jaringan syaraf tiruan. Prosiding FMIPA Universitas Pattimura; 2013; Ambon.
  13. Hernawan M, Sudjadi S, Warsito A. Simulasi kompresi citra dengan neural network menggunakan metode Self-Organizing Map [Disertasi]. Semarang: Fakultas Teknik Universitas Diponegoro; 2011.
  14. Lestari W. Sistem clustering kecerdasan majemuk mahasiswa menggunakan algoritma Self Organizing Maps (SOM). Jurnal Saintech Politeknik Indonusa Surakata. 2014;1(1).
  15. Elsalamony HA. Detection of anemia disease in human red blood cells using cell signature, neural networks and SVM. Multimed Tools Appl. 2018;77(12):15047-74. doi: 10.1007/s11042-017-5088-9
  16. Sayad S. Self-Organizing Maps (SOM). [series online] 2010 [cited 2018 Juli]. Available from: URL: https://www.saedsayad.com/clustering_som.htm
  17. Chaudhary V, Bhatia RS, Ahlawat AK. A novel Self-organizing map (SOM) learning algorithm with nearest and farthest neurons. Alexandria Engineering Journal. 2014;53(4):827-31. doi: 10.1016/j.aej.2014.09.007
  18. Umar R, Fadlil A, Az-Zahra RR. Pengelompokan peminatan jurusan di SMK menggunakan metode Self Organizing Map (SOM). Khazanah Informatika. 2018;4(2):131-7. doi: 10.23917/khif.v4i2.7044
  19. Usman A. Pengolahan citra digital & teknik pemrogramannya. Yogyakarta: Graha Ilmu; 2005.



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

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