Deep learning approach to detect malaria from microscopic images

Author:

Vijayalakshmi A ,Rajesh Kanna B ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference50 articles.

1. Abbas N, Mohamad D (2013) Microscopic RGB color images enhancement for blood cells segmentation in YCBCR color space for k-means clustering. J Theor Appl Inf Technol 55(1):117–125

2. Abu NS, Ashidi NMI, Chia LL, Mohamed Z, Kalthum UN, Zuhairi KZ (2008) Classification of malaria parasite species based on thin blood smears using multilayer perceptron network. Int J Comput Int Manag 16(1):46–52

3. Ahn E, Kumar A, Kim J, Li C, Feng D, Fulham M (2016) X-ray image classification using domain transferred convolutional neural networks and local sparse spatial pyramid, IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, 855–858

4. Anggraini D, Nugroho AS, Pratama C, Rozi IE, Pragesjvara V, Gunawan M (2011) Automated status identification of microscopic images obtained from malaria thin blood smears using bayes decision: a study case in Plasmodium falciparum. International Conference on Advanced Computer Science and Information Systems, Jakarta, pp 347–352

5. Arco JE, Gorriz JM, Ramírez J, Alvarez I, Puntonet CG (2015) Digital image analysis for automatic enumeration of malaria parasites using morphological operations. Expert Syst Appl 42:3041–3047

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