Classifying Parasitized and Uninfected Malaria Red Blood Cells Using Convolutional-Recurrent Neural Networks
Adán Antonio Alonso-Ramírez, Tat’y Mwata-Velu, Carlos H. García-Capulín, Horacio Rostro‐González, Juan Prado-Olivarez, Marcos Gutiérrez-López, Alejandro Israel Barranco Gutiérrez
Abstract
This work aims to classify malaria infected 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 an specialist interpretation of results to conclude the diagnosis process. Taking advantage of efficient deep learning approaches applied in computer vision field, the present framework propose two deep learning architecture based on 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 cell images was used for training and testing the proposed model. The methods developed in this work achieved an accuracy of 99.89 % in the detection of malaria cells infected, without preprocessing data.