CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Malaria Parasite Detection from Microscopic Blood Smear Image: A Literature Survey

by Thenu Eliza Thampi, Sreekumar K.
journal cover thumbnail

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

@article{ 10.5120/ijca2019918741,
author = { Thenu Eliza Thampi, Sreekumar K. },
title = { Malaria Parasite Detection from Microscopic Blood Smear Image: A Literature Survey },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2019 },
volume = { 182 },
number = { 48 },
month = { Apr },
year = { 2019 },
issn = { 0975-8887 },
pages = { 61-66 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number48/30523-2019918741/ },
doi = { 10.5120/ijca2019918741 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:14:43.382103+05:30
%A Thenu Eliza Thampi
%A Sreekumar K.
%T Malaria Parasite Detection from Microscopic Blood Smear Image: A Literature Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 48
%P 61-66
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
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
  1. Kunwar, S., Shrestha, M., & Shikhrakar, R.M. (2018). Malaria Detection Using Image Processing and Machine Learning. CoRR, abs/1801.10031.
  2. 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.
  3. 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.
  4. 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.
  5. Oliveira, Allisson Dantas, et al. "The malaria system microApp: a new, mobile device-based tool for malaria diagnosis." JMIR research protocols 6.4 (2017).
  6. 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.
  7. 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.
  8. Mustafa, Wan & Abdul Nasir, Aimi Salihah & Mohamed, Z. (2018). Malaria parasites segmentation based on sauvola algorithm modification. Malaysian Applied Biology. 47. 71-76.
  9. 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.
  10. 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.
  11. "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.
  12. 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

Computer Science
Information Sciences

Keywords

Blood smear image segmentation blood cells classification parasite detection

Powered by PhDFocusTM