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Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures
Resource type
Authors/contributors
- Gois, Francisco Nauber Bernardo (Author)
- Marques, João Alexandre Lobo (Author)
- de Oliveira Dantas, Allberson Bruno (Author)
- Santos, Márcio Costa (Author)
- Neto, José Valdir Santiago (Author)
- de Macêdo, José Antônio Fernandes (Author)
- Du, Wencai (Author)
- Li, Ye (Author)
- Lee, Roger (Editor)
Title
Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures
Abstract
Fast and efficient malaria diagnostics are essential in efforts to detect and treat the disease in a proper time. The standard approach to diagnose malaria is a microscope exam, which is submitted to a subjective interpretation. Thus, the automating of the diagnosis process with the use of an intelligent system capable of recognizing malaria parasites could aid in the early treatment of the disease. Usually, laboratories capture a minimum set of images in low quality using a system of microscopes based on mobile devices. Due to the poor quality of such data, conventional algorithms do not process those images properly. This paper presents the application of deep learning techniques to improve the accuracy of malaria plasmodium detection in the presented context. In order to increase the number of training sets, deep convolutional generative adversarial networks (DCGAN) were used to generate reliable training data that were introduced in our deep learning model to improve accuracy. A total of 6 experiments were performed and a synthesized dataset of 2.200 images was generated by the DCGAN for the training phase. For a real image database with 600 blood smears with malaria plasmodium, the proposed Deep Learning architecture obtained the accuracy of 100% for the plasmodium detection. The results are promising and the solution could be employed to support a mass medical diagnosis system.
Book Title
Computer and Information Science
Series
Studies in Computational Intelligence
Place
Cham
Publisher
Springer International Publishing
Date
2023
Pages
197-212
Language
en
ISBN
978-3-031-12127-2
Accessed
3/22/23, 6:27 AM
Library Catalog
Springer Link
Extra
Citation
Gois, F. N. B., Marques, J. A. L., de Oliveira Dantas, A. B., Santos, M. C., Neto, J. V. S., de Macêdo, J. A. F., Du, W., & Li, Y. (2023). Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures. In R. Lee (Ed.), Computer and Information Science (pp. 197–212). Springer International Publishing. https://doi.org/10.1007/978-3-031-12127-2_14
Academic Units
United Nations SDGs
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