Classifying Parasitized and Uninfected Malaria Red Blood Cells Using Convolutional-Recurrent Neural Networks

This work aims to classify malaria infected red blood cells from those uninfected using two deep learning approaches. Plasmodium parasite transmitted by a female anopheles's mosquitoes bite is the main cause of malaria. Commonly, Microbiological analyses by a microscope allows detecting cells infected from a blood sample, followed by a specialist interpretation of results to conclude the diagnosis process. Taking advantage of efficient deep learning approaches applied in computer vision field, the present framework proposes two deep learning architecture based on Convolutional-Recurrent neural Networks to detect accurately malaria infected cells. The first one implements a Convolutional Long Short-Term Memory while the second uses a Convolutional Bidirectional Long Short-Term Memory architecture. A malaria's public dataset consisting of parasitized and uninfected red blood cell images was used for training and testing the proposed models. The methods developed in this work achieved an accuracy of 99.89% in the detection of malaria-infected red blood cells, without preprocessing data.

Automated summary

This work utilizes two deep learning approaches to classify malaria infected red blood cells from uninfected ones. The proposed deep learning architectures, based on Convolutional-Recurrent neural Networks, achieved an accuracy of 99.89% in detecting malaria-infected red blood cells without preprocessing data, using a malaria's public dataset.