CLASSIFICATION of CELLS INFECTED with the MALARIA PARASITE with RESNET ARCHITECTURES

Abstract

Malaria is a disease that causes a parasite called plasmodium to be transmitted to humans as a result of the bite of female anopheles’ mosquitoes. Malaria is detected by examining the blood sample taken from the patient as a result of a microbiological examination under a microscope by specialist physicians. Although microscopy is widely used, its efficiency is low because it is time-consuming and depends on the interpretation of the specialist physician. In recent years, deep learning methods used in the field of computer vision increase the efficiency of specialist physicians by making a significant contribution to the decision-making process in solving real-life problems. In this study, ResNet architectures were preferred to quickly classify the malaria parasite using deep learning methods. For the training and testing of ResNet architectures, a dataset consisting of a total of 27558 red blood cell images containing 13779 parasitized and 13779 uninfected were used. Using this dataset, ResNet architectures were compared. As a result of the comparison, the best success accuracy (94.09%) was obtained with the ResNet-50 v2 model.

Keywords

• Deep learning
• Convolutional neural network
• ResNet
• Malaria parasite classification

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