Malaria Parasite Detection in Thin Blood Smear Images Using Deep Learning

Abstract

Algorithms in deep neural networks are being used to show the presence of malaria and to estimate the depth of infection by automatically counting individual uninfected and infected RBCs in images of thin blood smears. During the training period, the relationship was tried on a set of 13600 images from several thin blood spreads and experiment was conducted. My dataset is divided into 80% for training and 20% for testing. The relationship between the results from the algorithm and expert human readers was r = 0.866. Using image analysis results, the level of parasitemia may be achieved by applying methods to images of thin film smears. In this work, convolutional neural networks like CNN and ResNet are used for classification purposes and then classify them either infected or uninfected. Since my dataset contains only two prevalent species like Plasmodium vivax and P. falciparum that are predominant in their different cell stages and can cause 99% of the total infections. The results indicate varied distribution prevalence of P. vivax and P. falciparum, and also the mixed species infection due to these two species.

Keyword: Malaria parasite, deep learning, computer vision, intelligent systems, convolutional neural network.

Cite this Article: Rouf Ahmad Tantray, Zahoor Ahmad Najar. Malaria Parasite Detection in Thin Blood Smear Images Using Deep Learning. Journal of Artificial Intelligence Research & Advances. 2020; 7(1): 28–32p.