TY - JOUR
T1 - Classifying Parasitized and Uninfected Malaria Red Blood Cells Using Convolutional-Recurrent Neural Networks
AU - Alonso-Ramirez, Adan Antonio
AU - Mwata-Velu, Tat'Y
AU - Garcia-Capulin, Carlos Hugo
AU - Rostro-Gonzalez, Horacio
AU - Prado-Olivarez, Juan
AU - Gutierrez-Lopez, Marcos
AU - Barranco-Gutierrez, Alejandro Israel
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Bidirectional long short-term memory (BiLSTM)
KW - convolutional neural network (CNN)
KW - long short-term memory (LSTM)
KW - malaria dataset
UR - http://www.scopus.com/inward/record.url?scp=85139250473&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3206266
DO - 10.1109/ACCESS.2022.3206266
M3 - Article
AN - SCOPUS:85139250473
SN - 2169-3536
VL - 10
SP - 97348
EP - 97359
JO - IEEE Access
JF - IEEE Access
ER -