Image cropping for malaria parasite detection on heterogeneous data

Abstract

Malaria is a deadly disease of significant concern for the international community. It is an infectious disease caused by a Plasmodium spp. parasite and transmitted by the bite of an infected female Anopheles mosquito. The parasite multiplies in the liver and then destroys the person's red blood cells until it reaches the severe stage, leading to death. The most used tools for diagnosing this disease are the microscope and the rapid diagnostic test (RDT), which have limitations preventing control of the disease. Computer vision technologies present alternatives by providing the means for early detection of this disease before it reaches the severe stage, facilitating treatment and saving patients. In this article, we suggest deep learning methods for earlier and more accurate detection of malaria parasites with high generalization capabilities using microscopic images of blood smears from many heterogeneous patients. These techniques are based on an image preprocessing method that mitigates some of the challenges associated with the variety of red cell characteristics due to patient diversity and other artifacts present in the data.

For the study, we collected 65,970 microscopic images from 876 different patients to form a dataset of 33,007 images with a variety that enables us to create models with a high level of generalization. Three types of convolutional neural networks were used, namely Convolutional Neural Network (CNN), DenseNet, and LeNet-5, and the highest classification accuracy on the test data was 97.50% found with the DenseNet model.