@article{(Open Science Index):https://publications.waset.org/pdf/10011884,
	  title     = {Malaria Parasite Detection Using Deep Learning Methods},
	  author    = {Kaustubh Chakradeo and  Michael Delves and  Sofya Titarenko},
	  country	= {},
	  institution	= {},
	  abstract     = {Malaria is a serious disease which affects hundreds of
millions of people around the world, each year. If not treated in time,
it can be fatal. Despite recent developments in malaria diagnostics,
the microscopy method to detect malaria remains the most common.
Unfortunately, the accuracy of microscopic diagnostics is dependent
on the skill of the microscopist and limits the throughput of malaria
diagnosis. With the development of Artificial Intelligence tools and
Deep Learning techniques in particular, it is possible to lower the cost,
while achieving an overall higher accuracy. In this paper, we present a
VGG-based model and compare it with previously developed models
for identifying infected cells. Our model surpasses most previously
developed models in a range of the accuracy metrics. The model has
an advantage of being constructed from a relatively small number of
layers. This reduces the computer resources and computational time.
Moreover, we test our model on two types of datasets and argue
that the currently developed deep-learning-based methods cannot
efficiently distinguish between infected and contaminated cells. A
more precise study of suspicious regions is required.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {15},
	  number    = {2},
	  year      = {2021},
	  pages     = {175 - 182},
	  ee        = {https://publications.waset.org/pdf/10011884},
	  url   	= {https://publications.waset.org/vol/170},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 170, 2021},
	}