Malaria Detection Using Convolution Neural Network

Dr Balajee Maram; G. Hanitha

Dr Balajee Maram Associate Professor, Dept. of CSE, GMR Inst. Of Tech., Rajam, AP
G. Hanitha B.Tech 6th Semester Students, Dept. of CSE, GMR Inst. Of Tech., Rajam

Abstract

Malaria a disease which is infected from the bites of mosquitoes which are transmitted to the people caused by the parasites. The qualified technicians examination the microscopic blood smears for the parasite-infected red blood cells from the standard diagnosing method for malaria. The person who is doing the examination have to know the diagnosis method which is inefficient and the malaria which depends on the knowledge and experience. Using deep convolution neural network we have some of our recent research on highly accurate information of malaria which is infected the blood cells. The deep learning methods which have the advantage of being able to learn the features from the input data given. The CNN process is used with more accurate in determining the results and reduces the computation time. The cells of the microscopic images which are used for identifying the presence of malaria-infected parasites using image processing techniques, type of parasite and their stages of the malaria misdiagnosis can be considerably minimize the number deaths occurred.

How to cite this article:
Hanitha G, RajyaLakshmi A, Hemasree B et al. Malaria Detection using Convolution Neural Network J Adv Res Appl Arti Intel Neural Netw 2020; 4(1): 27-32.

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