Image Recognition of Malaria-infected Red Blood Cells among Other Normal and Cancer-Mutated Cells Using CNN
DOI:
https://doi.org/10.35877/454RI.jinav1552Keywords:
Deep learning, CNN, Malaria disease, Cancer-mutated cells, Dropout regularization.Abstract
Malaria is a contagious infectious disease that is still threatening human life. Malaria morbidity when viewed by province shows that Eastern Indonesia is the area with the highest Annual Parasite Incidence (API), namely Papua, West Papua, NTT, and Maluku. This is a concern for the continued efforts to control and eliminate malaria in these high malaria-endemic areas. There are many strategies to help and prevent, include the possibility of innovation in the diagnostic process. Therefore, to answer how to provide innovation in technology to accelerate the elimination of malaria, this study aims to identify the image of red blood cells which infected with malaria among other normal and leukemia cancer-mutated cells (non-malaria) by making improvements through the proposed new model used. This model is meant to do deep learning using Convolutional Neural Network (CNN). The results obtained in this study show that the success of using the proposed model is influenced by the pre-processing stage, the dropout regularization function, learning rate, and momentum value used. The accuracy value obtained is 0.9660, 0.9693 precision, 0.9626 recall, and an F1 score of 0.9659.
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Dang, N., Saraf, V., Khanna, A., Gupta, D., & Sheikh, T. H. (2018). Malaria Detection on Giemsa-Stained Blood Smears Using Deep Learning and Feature Extraction (Vol. 2). Springer.
Delgado-Ortet, M., Molina, A., Alférez, S., Rodellar, J., & Merino, A. (2020). A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection. Entropy, 22(6), 657. https://doi.org/10.3390/e22060657
Dong, Y., Jiang, Z., Shen, H., David Pan, W., Williams, L. A., Reddy, V. V. B., Benjamin, W. H., & Bryan, A. W. (2017). Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells. 2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017, 101–104. https://doi.org/10.1109/BHI.2017.7897215
Dong, Y., Pan, W. D., & Wu, D. (2019). Impact of misclassification rates on compression efficiency of red blood cell images of malaria infection using deep learning. Entropy, 21(11). https://doi.org/10.3390/e21111062
Fatima, M., & Pasha, M. (2017). Survey of Machine Learning Algorithms for Disease Diagnostic. Journal of Intelligent Learning Systems and Applications, 09(01), 1–16. https://doi.org/10.4236/jilsa.2017.91001
Gopakumar, G. P., Swetha, M., Sai Siva, G., & Sai Subrahmanyam, G. R. K. (2018). Convolutional neural network-based malaria diagnosis from focus stack of blood smear images acquired using custom-built slide scanner. In Journal of Biophotonics (Vol. 11, Issue 3). https://doi.org/10.1002/jbio.201700003
Jan, Z., Khan, A., Sajjad, M., Muhammad, K., Rho, S., & Mehmood, I. (2018). A review on automated diagnosis of malaria parasite in microscopic blood smears images. Multimedia Tools and Applications, 77(8), 9801–9826. https://doi.org/10.1007/s11042-017-4495-2
Khedkar, S., Gandhi, P., Shinde, G., & Subramanian, V. (2020). Deep Learning Techniques for Biomedical and Health Informatics. https://doi.org/10.1007/978-3-030-33966-1_7
Kumar, R., Singh, S. K., & Khamparia, A. (2018). Malaria Detection Using Custom Convolutional Neural Network Model on Blood Smear Slide Images. In International Conference on Advanced Informatics for Computing Research (Vol. 956). https://doi.org/10.1007/978-981-13-3143-5
Liang, Z., Powell, A., Ersoy, I., Poostchi, M., Silamut, K., Palaniappan, K., Guo, P., Hossain, M. A., Sameer, A., Maude, R. J., Huang, J. X., Jaeger, S., & Thoma, G. (2017). CNN-based image analysis for malaria diagnosis. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, 493–496. https://doi.org/10.1109/BIBM.2016.7822567
Madhu, G. (2020). Computer Vision and Machine Learning Approach for Malaria Diagnosis in Thin Blood Smears from Microscopic Blood Images.
Mustafa, W. A., Santiagoo, R., Jamaluddin, I., Othman, N. S., Khairunizam, W., & Rohani, M. N. K. H. (2018). Comparison of Detection Method on Malaria Cell Images. 2018 International Conference on Computational Approach in Smart Systems Design and Applications, ICASSDA 2018, 1–6. https://doi.org/10.1109/ICASSDA.2018.8477624
Pattanaik, P. A., Mittal, M., Khan, M. Z., & Panda, S. N. (2020). Malaria detection using deep residual networks with mobile microscopy. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2020.07.003
Poostchi, M., Silamut, K., Maude, R. J., Jaeger, S., & Thoma, G. (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
Pusat Data Kesehatan dan Informasi, Indonesia, Kementerian Kesehatan Republik, I. 2442-7659. (2016). InfoDatin-Malaria-2016.pdf.
Quan, Q., Wang, J., & Liu, L. (2020). An Effective Convolutional Neural Network for Classifying Red Blood Cells in Malaria Diseases. Interdisciplinary Sciences: Computational Life Sciences, 12(2), 217–225. https://doi.org/10.1007/s12539-020-00367-7
Rahman, A., Zunair, H., Rahman, M. S., Yuki, J. Q., Biswas, S., Alam, M. A., Alam, N. B., & Mahdy, M. R. C. (2019). Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networks. 1–33. http://arxiv.org/abs/1907.10418
Razzak, M. I., Naz, S., & Zaib, A. (2018). Deep Learning for Medical Image Processing: Overview, Challenges and the Future BT - Classification in BioApps: Automation of Decision Making. Springer, 323–350. https://doi.org/10.1007/978-3-319-65981-7_12
Sai Bharadwaj Reddy, A., & Sujitha Juliet, D. (2019). Transfer learning with RESNET-50 for malaria cell-image classification. Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, 945–949. https://doi.org/10.1109/ICCSP.2019.8697909
Shekar, G., Revathy, S., & Goud, E. K. (2020). Malaria Detection using Deep Learning. Icoei, 746–750. https://doi.org/10.1109/icoei48184.2020.9143023
Sunarko, B., Djuniadi, Bottema, M., Iksan, N., Hudaya, K. A. N., & Hanif, M. S. (2020). Red blood cell classification on thin blood smear images for malaria diagnosis. Journal of Physics: Conference Series, 1444(1). https://doi.org/10.1088/1742-6596/1444/1/012036
Suriya, M., Chandran, V., & Sumithra, M. G. (2019). Enhanced deep convolutional neural network for malarial parasite classification. International Journal of Computers and Applications, 0(0), 1–10. https://doi.org/10.1080/1206212X.2019.1672277
Vijayalakshmi A, & Rajesh Kanna B. (2020). Deep learning approach to detect malaria from microscopic images. Multimedia Tools and Applications, 79(21–22), 15297–15317. https://doi.org/10.1007/s11042-019-7162-y
Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018). Deep Learning for Computer Vision: A Brief Review. Computational Intelligence and Neuroscience, 2018. https://doi.org/10.1155/2018/7068349
Widiawati, C. R. A., Nugroho, H. A., & Ardiyanto, I. (2016). Plasmodium detection methods in thick blood smear images for diagnosing Malaria: A review. Proceedings - 2016 1st International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2016, 142–147. https://doi.org/10.1109/ICITISEE.2016.7803063
World Health Organization. (2019a). World Malaria Report. https://www.who.int/publications-detail/world-malaria-report-2019
World Health Organization. (2019b). World Malaria Report 2019. Geneva. December, 1–232. https://www.who.int/publications-detail/world-malaria-report-2019
Yang, F., Yu, H., Silamut, K., Maude, R. J., Jaeger, S., & Antani, S. (2019). Smartphone-Supported Malaria Diagnosis Based on Deep Learning (Vol. 1). https://doi.org/10.1007/978-3-030-32692-0
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Published
2022-07-31
How to Cite
Nivaan, G. V. (2022). Image Recognition of Malaria-infected Red Blood Cells among Other Normal and Cancer-Mutated Cells Using CNN. JINAV: Journal of Information and Visualization, 3(1), 62–70. https://doi.org/10.35877/454RI.jinav1552
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