Implementation of Convolutional Neural Network in the classification of red blood cells have affected of malaria

Authors

  • Mawaddah Harahap Universitas Prima Indonesia
  • Jefferson Jefferson Universitas Prima Indonesia
  • Surya Barti Universitas Prima Indonesia
  • Suprianto Samosir Universitas Prima Indonesia
  • Christi Andika Turnip Universitas Prima Indonesia

DOI:

10.33395/sinkron.v5i2.10713

Abstract

Malaria is a disease caused by plasmodium which attacks red blood cells. Diagnosis of malaria can be made by examining the patient's red blood cells using a microscope. Convolutional Neural Network (CNN) is a deep learning method that is growing rapidly. CNN is often used in image classification. The CNN process usually requires considerable resources. This is one of the weaknesses of CNN. In this study, the CNN architecture used in the classification of red blood cell images is LeNet-5 and DRNet. The data used is a segmented image of red blood cells and is secondary data. Before conducting the data training, data pre-processing and data augmentation from the dataset was carried out. The number of layers of the LeNet-5 and DRNet models were 4 and 7. The test accuracy of the LeNet-5 and DrNet models was 95% and 97.3%, respectively. From the test results, it was found that the LeNet-5 model was more suitable in terms of red blood cell classification. By using the LeNet-5 architecture, the resources used to perform classification can be reduced compared to previous studies where the accuracy obtained is also the same because the number of layers is less, which is only 4 layers

GS Cited Analysis

Downloads

Download data is not yet available.

References

Abiyev, Rahib H., and Mohammad Khaleel Sallam Ma’aitah. 2018. “Deep Convolutional Neural Networks for Chest Diseases Detection.” Journal of Healthcare Engineering 2018.

Autino, Beatrice, Alice Noris, Rosario Russo, and Francesco Castelli. 2012. “Epidemiology of Malaria in Endemic Areas.” Mediterranean Journal of Hematology and Infectious Diseases 4(1).

Chima, Jaspreet Singh, Abhishek Shah, Karan Shah, and Rekha Ramesh. 2020. “Malaria Cell Image Classification Using Deep Learning.” International Journal of Recent Technology and Engineering 8(6): 5553–59.

Dave, Ishan R. 2018. “Image Analysis for Malaria Parasite Detection from Microscopic Images of Thick Blood Smear.” Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017 2018-Janua: 1303–7.

Hidayat, Ardi, Ucuk Darusalam, and Irmawati Irmawati. 2019. “Detection of Disease on Corn Plants Using Convolutional Neural Network Methods.” Jurnal Ilmu Komputer dan Informasi 12(1): 51.

Ignatius, Hartanto, Ricky Chandra, Nicholas Bohdan, and Abdi Dharma. 2019. “COMPARISON OF CONVOLUTIONAL NEURAL NETWORK MODEL IN CLASSIFICATION OF DIABETIC RETINOPATHY.” Jurnal Penelitian Pos dan Informatika 9(2): 141–50.

Kalkan, Soner Can, and Ozgur Koray Sahingoz. 2019. “Deep Learning Based Classification of Malaria from Slide Images.” 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019: 1–4.

Liang, Zhaohui et al. 2017. “CNN-Based Image Analysis for Malaria Diagnosis.” Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016: 493–96.

Nanoti, Akshay, Sparsh Jain, Chetan Gupta, and Garima Vyas. 2016. “Detection of Malaria Parasite Species and Life Cycle Stages Using Microscopic Images of Thin Blood Smear.” Proceedings of the International Conference on Inventive Computation Technologies, ICICT 2016 1.

Poostchi, Mahdieh et al. 2018. “Image Analysis and Machine Learning for Detecting Malaria.” Translational Research 194(2018): 36–55. https://doi.org/10.1016/j.trsl.2017.12.004.

Rahman, Aimon et al. 2019. “Improving Malaria Parasite Detection from Red Blood Cell Using Deep Convolutional Neural Networks.” Https://Arxiv.Org/Ftp/Arxiv/Papers/1907/1907.10418.Pdf: 1–33.

Rahmanti, Farah Zakiyah et al. 2014. “LVQ (Learning Vector Quantization) Method for Identification of Plasmodium Vivax in Thick Blood Film.” Icbeta (October 2015).

Rajaraman, Sivaramakrishnan et al. 2018. “Pre-Trained Convolutional Neural Networks as Feature Extractors toward Improved Malaria Parasite Detection in Thin Blood Smear Images.” PeerJ 2018(4): 1–17.

Sankaran, S et al. 2017. “Detection and Classification of Malaria Parasites Using Digital Image Processing.” international Research Journal of Engineering and Technology (IRJET) 04(05): 87–89.

Singh, Amartya et al. 2020. “Malaria Detection Using Contour Detection And Random Forest Classifier.” 29(3): 503–13.

Yadav, Samir S., and Shivajirao M. Jadhav. 2019. “Deep Convolutional Neural Network Based Medical Image Classification for Disease Diagnosis.” Journal of Big Data 6(1). https://doi.org/10.1186/s40537-019-0276-2.

Yohannes, Yohannes, Siska Devella, and Kelvin Arianto. 2020. “Deteksi Penyakit Malaria Menggunakan Convolutional Neural Network Berbasis Saliency.” JUITA: Jurnal Informatika 8(1): 37.

Downloads


Crossmark Updates

How to Cite

Harahap, M., Jefferson, J., Barti, S., Samosir, S., & Turnip, C. A. (2021). Implementation of Convolutional Neural Network in the classification of red blood cells have affected of malaria . Sinkron : Jurnal Dan Penelitian Teknik Informatika, 5(2), 199-207. https://doi.org/10.33395/sinkron.v5i2.10713