4.6 Article

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

期刊

IEEE ACCESS
卷 10, 期 -, 页码 97348-97359

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3206266

关键词

Diseases; Convolutional neural networks; Cells (biology); Deep learning; Microscopy; Data models; Support vector machines; Recurrent neural networks; Long short term memory; Bidirectional long short-term memory (BiLSTM); convolutional neural network (CNN); long short-term memory (LSTM); malaria dataset

资金

  1. Mexican National Council of Science and Technology Consejo Nacional de Ciencia y Tecnologia (CONACYT), Spanish [725022]
  2. Tecnologico Nacional de Mexico (TecNM) en Celaya [D2203002]
  3. Engineering Division of the University of Guanajuato

向作者/读者索取更多资源

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.
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.

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