Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks-Reference-Cited by-同舟云学术

Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks

Published:2018-09-19 Issue: Volume: Page:
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Container-title:Machine Learning - Advanced Techniques and Emerging Applications
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Author:

Pan W. David,Dong Yuhang,Wu Dongsheng

Publisher

InTech

Link

http://www.intechopen.com/download/pdf/58989

Reference35 articles.

1. Chan M. World Malaria Report. Geneva: World Health Organization; 2015

2. Kettlehut MM et al. External quality assessment schemes raise standards: Evidence from the UKNEQAS parasitology subschemes. Journal of Clinical Pathology. 2003 Dec;56:927-932

3. Tek FB. Computerised diagnosis of malaria [PhD Thesis]. University of Westminster; 2007

4. Tek FB, Dempster AG, Kale I. Computer vision for microscopy diagnosis of malaria. Malaria Journal. 2009 Dec;8(1):1-14

5. Mitiku K, Mengistu G, Gelaw B. The reliability of blood film examination for malaria at the peripheral health unit. Ethiopian Journal of Health Development. 2003;17(3):197-204

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