The week's pick
Random Articles
Reseach Article
Malaria Parasite Detection from Microscopic Blood Smear Image: A Literature Survey
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 48 |
| Year of Publication: 2019 |
| Authors: Thenu Eliza Thampi, Sreekumar K. |
10.5120/ijca2019918741
|
Thenu Eliza Thampi, Sreekumar K. . Malaria Parasite Detection from Microscopic Blood Smear Image: A Literature Survey. International Journal of Computer Applications. 182, 48 ( Apr 2019), 61-66. DOI=10.5120/ijca2019918741
Abstract
Malaria is a mosquito-borne parasitic disease that is caused by the parasite of the genus Plasmodium. This infectious disease is transmitted via the bite of infected Anopheles mosquitoes. This allows the parasite to enter the human body, and they get matured in the liver and affect the RBC. Malaria is usually found in tropical regions and sub-tropical regions where the climate is suitable for its growth. Every year millions of people are affected by Malaria. In this paper, we focus on the study of different image processing methods for the detection of Malaria infection in humans. A comparison study is made among these methods.
References
- Kunwar, S., Shrestha, M., & Shikhrakar, R.M. (2018). Malaria Detection Using Image Processing and Machine Learning. CoRR, abs/1801.10031.
- Kanafiah, S. N. A. M., et al. "An Intelligent Recognition Procedure for Trophozoite Stages of Plasmodium Knowlesi Malaria." Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 10.1-16 (2018): 31-35.
- Oliveira, Allisson Dantas, et al. "An automatic system for computing malaria parasite density in thin blood films." Iberoamerican Congress on Pattern Recognition. Springer, Cham, 2017.
- Abdul-Nasir, Aimi Salihah, et al. "The cascaded moving k-means and fuzzy c-means clustering algorithms for unsupervised segmentation of malaria images." AIP Conference Proceedings. Vol. 1660. No. 1. AIP Publishing, 2015.
- Oliveira, Allisson Dantas, et al. "The malaria system microApp: a new, mobile device-based tool for malaria diagnosis." JMIR research protocols 6.4 (2017).
- Bias, Stephen, and Izzet Kale. "Mobile Hardware Based Implementation of a Novel, Efficient, Fuzzy Logic Inspired Edge Detection Technique for Analysis of Malaria Infected Microscopic Thin Blood Images." Procedia Computer Science 141 (2018): 374-381.
- Hussain, Mahmood, and Dibya JyotiBora. "An Analytical Study on Different Image Segmentation Techniques for Malaria Parasite Detection." 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE). IEEE, 2018.
- Mustafa, Wan & Abdul Nasir, Aimi Salihah & Mohamed, Z. (2018). Malaria parasites segmentation based on sauvola algorithm modification. Malaysian Applied Biology. 47. 71-76.
- Rajaraman, Sivaramakrishnan, et al. "Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images." PeerJ 6 (2018): e4568.
- Bibin, Dhanya, Madhu S. Nair, and P. Punitha. "Malaria parasite detection from peripheral blood smear images using deep belief networks." IEEE Access 5 (2017): 9099-9108.
- "Life cycle of the malaria parasite" from Epidemiology of Infectious Diseases. Available at: http://ocw.jhsph.edu. Copyright © Johns Hopkins Bloomberg School of Public Health. Creative Commons BY-NC-SA.
- Rajaraman S, Antani SK, Poostchi M, Silamut K, Hossain MA, Maude, RJ, Jaeger S, Thoma GR. (2018) Pre-trained convolutional neural networks as feature extractors toward improved Malaria parasite detection in thin blood smear images. PeerJ6:e4568. .
Index Terms
Keywords