Detection Malaria Base Microscope Digital Image with Convolutional Neural Network

Authors

  • Meida Cahyo Untoro Institut Teknologi Sumatera
  • Muhammad Muttaqin

DOI:

10.33395/sinkron.v7i3.11488

Keywords:

CNN, Image, Malaria, Parasite, Microscope

Abstract

Malaria is a tropical disease that infects human red blood cells caused by infection with the plasmodium parasite. Plasmodium parasites spread to humans through female Anopheles mosquitoes and can reproduce in human blood cells. Malaria is a health problem that is at risk of causing other health problems such as anemia and even death. The current gold standard for malaria diagnosis is laboratory diagnosis by microscopic examination to find the malaria parasite through the blood cells of the patient. However, the diagnosis of malaria through microscopic observation of blood cells has the potential to take a long time, because the plasmodium parasite has a very small size. The malaria detection system using the Convolutional Neural Network (CNN) method is designed to detect malaria in human blood cells. CNN is a machine learning method designed to classify objects in an image. The system was built in three stages of development, namely the development of a CNN model for malaria detection, software development and hardware development. The hardware components used in the system include Raspberry pi, Raspberry Pi camera module, and LCD. The results of the malaria detection test using the CNN model gave an accuracy of 98.76% which was tested on blood cell images from a microscope

GS Cited Analysis

Downloads

Download data is not yet available.

References

Abdullah, L. A., & Al-Ani, M. S. (2020). CNN-LSTM based model for ECG arrhythmias and myocardial infarction classification. Advances in Science, Technology and Engineering Systems, 5(5), 601–606. https://doi.org/10.25046/AJ050573

Aldahoul, N., Karim, H. A., Abdullah, M. H. L., Wazir, A. S. B., Fauzi, M. F. A., Tan, M. J. T., Mansor, S., & Lyn, H. S. (2021). An Evaluation of Traditional and CNN-Based Feature Descriptors for Cartoon Pornography Detection. IEEE Access, 9, 39910–39925. https://doi.org/10.1109/ACCESS.2021.3064392

Arowolo, M. O., Adebiyi, M., Adebiyi, A., & Okesola, O. (2020). PCA Model for RNA-Seq Malaria Vector Data Classification Using KNN and Decision Tree Algorithm. 2020 International Conference in Mathematics, Computer Engineering and Computer Science, ICMCECS 2020, March. https://doi.org/10.1109/ICMCECS47690.2020.240881

Banyal, N. A., & Dayat, A. R. (2016). Malaria Dalam Sel Darah Merah Manusia Dengan Menggunakan Metode Multi Class Support Vector Machine (SVM). Jurnal Ilmiah ILKOM, 8(Agustus), 111–118. https://media.neliti.com/media/publications/258814-klasifikasi-citra-plasmodium-penyebab-pe-d8c974da.pdf

Febrianti, E. L., & Christi, T. (2017). Peneraan Forward Chaining Untuk Mendianogsa Penyakit Malaria Dan Pencegahanya Berbasis Web. Jurteksi, 4(1), 93–100. https://doi.org/10.33330/jurteksi.v4i1.32

Hamid, M., Mudjirahardjo, P., & Yudaningtyas, E. (2016). Penerapan Fitur Warna Untuk Identifikasi Plasmodium Falciparum pada Sediaan Apus. MATICS : Jurnal Ilmu Komputer Dan Teknologi Informasi, 8(2), 73–77.

Kemenkes RI. (2021). Malaria: Penyebab Kematian Tertinggi di Dunia. Online, 1–3. https://www.malaria.id/profil

Mishra, S. K. (2021). Human Malaria Detection and Stage Classification using Random Forest Classifier. 6(6), 214–218.

Pangestu, M. A., & Bunyamin, H. (2018). Analisis Performa dan Pengembangan Sistem Deteksi Ras Anjing pada Gambar dengan Menggunakan Pre-Trained CNN Model. Jurnal Teknik Informatika Dan Sistem Informasi, 4, 337–344.

Pratiwi, N. K. C., Ibrahim, N., Fu’Adah, Y. N., & Rizal, S. (2021). Deteksi Parasit Plasmodium pada Citra Mikroskopis Hapusan Darah dengan Metode Deep Learning. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 9(2), 306. https://doi.org/10.26760/elkomika.v9i2.306

Puasa, R., H, A. A., & Kader, A. (2018). Identifikasi Plasmodium Malaria Didesa Beringin Jaya Kecamatan Oba Tengah Kota Tidore Kepulauan. Jurnal Riset Kesehatan, 7(1), 21. https://doi.org/10.31983/jrk.v7i1.3056

Raspberry_Pi_Trading_Ltd. (2019). Karta katalogowa - Raspberry Pi 4 Computer Model B. June. www.raspberrypi.org

Setiawan, A. W., Rahman, Y. A., Faisal, A., Siburian, M., Resfita, N., Gifari, M. W., & Setiawan, R. (2021). Deteksi Malaria Berbasis Segmentasi Warna Citra dan Pembelajaran Mesin. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(4), 769. https://doi.org/10.25126/jtiik.2021844377

Susanti, I., Handayani, S., Ekowatiningsih, R., Prasetyorini, B., Yusnita, E. A., Ardianto, D. A., & Widjaya, S. K. (2017). Pengembangan Mikroskop dengan Mikrokontroler dan Cahaya Monokromatis untuk Mendeteksi Parasit Malaria. Jurnal Teknologi Laboratorium, 6(2), 75–82. https://doi.org/https://doi.org/10.29238/teknolabjournal.v6i2.59

Untoro, M. C., & Buliali, J. L. (2018). Penanganan imbalance class data laboratorium kesehatan dengan Majority Weighted Minority Oversampling Technique. Register: Jurnal Ilmiah Teknologi Sistem Informasi, 4(1), 23. https://doi.org/10.26594/register.v4i1.1184

Yohannes, Y., Devella, S., & Arianto, K. (2020). Deteksi Penyakit Malaria Menggunakan Convolutional Neural Network Berbasis Saliency. JUITA: Jurnal Informatika, 8(1), 37. https://doi.org/10.30595/juita.v8i1.6671

Downloads


Crossmark Updates

How to Cite

Untoro, M. C., & Muttaqin, M. (2022). Detection Malaria Base Microscope Digital Image with Convolutional Neural Network. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(3), 935-943. https://doi.org/10.33395/sinkron.v7i3.11488