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

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 c...

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Bibliographic Details
Main Authors: Adan Antonio Alonso-Ramirez, Tat'y Mwata-Velu, Carlos Hugo Garcia-Capulin, Horacio Rostro-Gonzalez, Juan Prado-Olivarez, Marcos Gutierrez-Lopez, Alejandro Israel Barranco-Gutierrez
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Bidirectional long short-term memory (BiLSTM)
convolutional neural network (CNN)
long short-term memory (LSTM)
malaria dataset
Online Access:https://ieeexplore.ieee.org/document/9888070/

Internet

https://ieeexplore.ieee.org/document/9888070/

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