Open AccessDiagnosis of Malaria from Peripheral Blood Smear Images using Convolutional Neural Networks
Author(s) -
Dr.M. Mohana*,
V. Vani,
Shri Dikshanya K.N,
B M Shruthi,
V R Vinothini
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d9983.118419
Subject(s) - malaria , blood smear , peripheral blood , convolutional neural network , plasmodium vivax , diagnosis of malaria , artificial intelligence , population , blood film , plasmodium falciparum , parasite hosting , computer science , immunology , medicine , environmental health , world wide web
Malaria is a deadly disease brought about by Plasmodium parasites which affects the general population through the bites of female mosquitoes, called "malaria vectors." There are about five parasites species that cause malaria in human body, and two of the species namely P. falciparum , P.vivax pose the greatest threat. The most prominent technique to detect malaria is by taking blood smear samples to check if the RBC is affected by parasite under the microscope by qualified experts. It is a complex technique and the diagnosis depends on the experience and inside of the person who performs the examination. Malaria blood smear have been diagnosed earlier using image processing methods based on machine learning. This was not effective so far. Convolutional Neural Network (CNN) is use in this system which helps in classifying the cells present in the blood smear images as infected or uninfected