Home   >   CSC-OpenAccess Library   >    Manuscript Information
Malarial Parasite Classification using Recurrent Neural Network
Imran Razzak
Pages - 69 - 79     |    Revised - 01-03-2015     |    Published - 31-03-2015
Volume - 9   Issue - 2    |    Publication Date - March / April 2015  Table of Contents
MORE INFORMATION
KEYWORDS
Malaria Detection, Segmentation, RBC Classification, Blood Cell Analysis.
ABSTRACT
Malaria parasite detection relies mainly on the manual examination of Giemsa-stained blood microscopic slides whereas it is very long, tedious, and prone to error. Automatic malarial parasite analysis and classification has opened a new area for the early malaria detection that showed potential to overcome the drawbacks of manual strategies. This paper presented a method for automatic detection of falciparum and vivax plasmodium. Although, malaria cell segmentation and morphological analysis is a challenging problem due to both the complex cell nature uncertainty in microscopic videos. To improve the performance of malaria parasite segmentation and classification, segmented the RBC and used RNN for classification into its type. Segmented RBCs are classified into normal RBC and infected cell. RNN identify the infected cells into further types.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
A. Kumar, A. Choudhary, P. U. Tembhare, and C. R. Pote, Enhanced identification of malarial infected objects using otsu algorithm from thin smear digital images. International Journal of Latest Research in Science and Technology ISSN (Online):2278-5299, 1, 159 (2012).
A. Mehrjou and T. Abbasian, Automatic Malaria Diagnosis System, First International Conference on RSI/ISM.
Bates, V. Bekoe, and A. Asamoa-Adu, Improving the accuracy of malaria related laboratory tests in Ghana. Malar J. 3, 38 (2004).
C. D. Ruberto, A. Dempster, and S. B. Khan Jarra, Analysis of infected blood cell images using morphological operators. Image and Computer Vision 20 (2002).
Chan LL, Laverty DJ, Smith T, Nejad P, Hei H, Gandhi R, Kuksin D, Qiu J., Accurate measurement of peripheral blood mononuclear cell concentration using image cytometry to eliminate RBC-induced counting error” , Journal of Immunological Method, Vol. 238, pp 25 32, 2013 Feb
Chan, Y.-K., Tsai, M.-H., Huang, D. C., Zheng, Z.-H., Hung, K.-D., 2010. Leukocyte nucleus segmentation and nucleus lobe counting. BMC Bioinformatics 11, 558.
D. K. Das, M. Ghosh, M. Pal, A. K. Maiti, and C. Chakraborty, Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron 45, 97 (2013).
D. M. Memeu, K. A. Kaduki, and A. C. K. Mjomba, Njogu Samson Muriuki,Lucy Gitonga, Detection of plasmodium parasites from images of thin blood smears. Open Journal of Clinical Diagnostics 3, 183 (2013).
D. M. U. Sabino, L. da Fontoura Costa, E. Gil Rizzatti, and M. Antonio Zago, A texture approach to leukocyte recognition. Real-Time Imaging 10, 205 (2004).
F. B. Tek, A. G. Dempster, and I. Kale, Malaria parasite detection in peripheral blood images. Proceeding of British Machine Vision Conference (2006).
F. Costa L da and R. Marcondes, Jr., Shape analysis and classification: theory and practice. Boca Raton, FL: CRC Press (2001).
G. Díaz, F. A. González, and E. Romero, A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images. Journal of Biomedical Informatics 42, 296 (2009).
Gloria Díaz, Fabio Gonzalez, and Eduardo Romero, Infected cell identification in thin blood images based on color pixel classification: Comparison and analysis. Lecture Notes in Computer Science 4756, 812 (2007).
K. Mitiku, G. Mengistu, and B. Gelaw, The reliability of blood film examination for malaria at the peripheral health unit. Ethiopian J. of Health Dev. 17, 197 (2003).
M. I. Khan, B. Acharya, B. K. Singh, and J. Soni, Content based image retrieval approaches for detection of malarial parasite in blood images. International Journal of Biometrics and Bioinformatics (IJBB) 5 (2011).
M. M. Kettelhut, P. L. Chiodini, and H. Edwards, Moody A: External quality assessment schemes raise standards: evidence from the UKNEQAS parasitology subschemes. J. Clin. Pathol. 56, 927 (2003).
M.I.Razzak, B. AlHaqbani, “Automatic Detection of Malarial Parasite Using Microscopic Blood Images” Journal of Medical Imaging and Health Informatics Vol. 5, 1–8, 2015
Muhammad Imran Razzak, “Automatic Detection and Classification of Malarial Parasite”, International Journal of Biometrics and Bioinformatics (IJBB) Volume-9 Issue-1, 2015.
N. Ahirwar, S. Pattnaik1, and B. Acharya, Advanced image analysis based system for automatic detection and classification of malarial parasite in blood mages. International Journal of Information Technology and Knowledge Management 5, 59 (2012).
R. E. Coleman, N. Maneechai, N. Rachaphaew, C. Kumpitak, R. Miller, V. Soyseng, K. Thimasarn, and J. Sattabongkot, Comparison of field and expert laboratory microscopy for active surveillance for asymptomatic plasmodium falciparum and plasmodium vivax in western thailand. Am. J. Trop Med. Hyg. 67, 141 (2002).
R. Sriram, M. Chandar, and K. Srinivas, Computer aided malarial diagnosis for JSB stained white light images using neural networks. International Journal of Advanced Research in Computer Science and Software Engineering 3 (2013).
S. H. Rezatofighti and H. Soltanian-Zadeh, Automatic Recognition of Five types of White blood cell in peripheral blood. Computerized Medical Imaging and Graphics, Volume 35, Issue 4, Pages 333–343, June 2011.
S. Kaewkamnerd and C. Uthaipibull, Apichart Intarapanich, Montri Pannarut, Sastra Chaotheing, Sissades Tongsima, An automatic device for detection and classification of malaria parasite species in thick blood film. BMC Bioinformatics 13, S18 (Suppl 17) (2012).
S. S. Savkare and S. P. Narote, Automatic detection of malaria parasites for estimating parasitemia. International Journal of Computer Science and Security (IJCSS), 5 (2011).
T. Chen, YongZhang, C. Wang, ZhenshenQu, FeiWang, and Tanveer Syeda-Mahmood, Complex local phase based subjective surfaces (CLAPSS) and its application to DIC red blood cell image segmentation. Neurocomputing 99, 98 (2013).
T. P. Suradkar, Detection of malarial parasite in blood using image processing. International Journal of Engineering and Innovative Technology (IJEIT) 2, (2013).
W. S. Selena Sio, W. Sun, Saravana Kumar, W. Z. Bin, S. S. Tan, S. H. Ong, H. Kikuchi, Y. Oshima, and K. S. W. Tan, Malaria Count: An image analysis-based program for the accurate determination of parasitemia. Journal of Microbiological Methods 68, 11 (2007).
World Health Organization: World Malaria Report 2011. Geneva; 2011
Yunda L., Ramirez, A.A., Millan, J., Automated Image Analysis Method for p-vivax Malaria Parasite Detection in Thick Film Blood Images, Revista S&T, 10(20), 9-25
Z. Karel, Contrast limited adaptive histogram equalization. Graphics Gems IV 474–485 (1994), (code: 479–484).
Dr. Imran Razzak
KSAU-HS - Saudi Arabia
razzakmu@ngha.med.sa


CREATE AUTHOR ACCOUNT
 
LAUNCH YOUR SPECIAL ISSUE
View all special issues >>
 
PUBLICATION VIDEOS