Cargando…

Multiclass malaria parasite recognition based on transformer models and a generative adversarial network

Malaria is an extremely infectious disease and a main cause of death worldwide. Microscopic examination of thin slide serves as a common method for the diagnosis of malaria. Meanwhile, the transformer models have gained increasing popularity in many regions, such as computer vision and natural langu...

Descripción completa

Detalles Bibliográficos
Autores principales: Tan, Dianhuan, Liang, Xianghui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10564789/
https://www.ncbi.nlm.nih.gov/pubmed/37816938
http://dx.doi.org/10.1038/s41598-023-44297-y
_version_ 1785118553472499712
author Tan, Dianhuan
Liang, Xianghui
author_facet Tan, Dianhuan
Liang, Xianghui
author_sort Tan, Dianhuan
collection PubMed
description Malaria is an extremely infectious disease and a main cause of death worldwide. Microscopic examination of thin slide serves as a common method for the diagnosis of malaria. Meanwhile, the transformer models have gained increasing popularity in many regions, such as computer vision and natural language processing. Transformers also offer lots of advantages in classification task, such as Fine-grained Feature Extraction, Attention Mechanism etc. In this article, we propose to assist the medical professionals by developing an effective framework based on transformer models and a generative adversarial network for multi-class plasmodium classification and malaria diagnosis. The Generative Adversarial Network is employed to generate extended training samples from multiclass cell images, with the aim of enhancing the robustness of the resulting model. We aim to optimize plasmodium classification to achieve an exact balance of high accuracy and low resource consumption. A comprehensive comparison of the transformer models to the state-of-the-art methods proves their efficiency in the classification of malaria parasite through thin blood smear microscopic images. Based on our findings, the Swin Transformer model and MobileVit outperform the baseline architectures in terms of precision, recall, F1-score, specificity, and FPR on test set (the data was divided into train: validation: test splits). It is evident that the Swin Transformer achieves superior detection performance (up to 99.8% accuracy), while MobileViT demonstrates lower memory usage and shorter inference times. High accuracy empowers healthcare professionals to conduct precise diagnoses, while low memory usage and short inference times enable the deployment of predictive models on edge devices with limited computational and memory resources.
format Online
Article
Text
id pubmed-10564789
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-105647892023-10-12 Multiclass malaria parasite recognition based on transformer models and a generative adversarial network Tan, Dianhuan Liang, Xianghui Sci Rep Article Malaria is an extremely infectious disease and a main cause of death worldwide. Microscopic examination of thin slide serves as a common method for the diagnosis of malaria. Meanwhile, the transformer models have gained increasing popularity in many regions, such as computer vision and natural language processing. Transformers also offer lots of advantages in classification task, such as Fine-grained Feature Extraction, Attention Mechanism etc. In this article, we propose to assist the medical professionals by developing an effective framework based on transformer models and a generative adversarial network for multi-class plasmodium classification and malaria diagnosis. The Generative Adversarial Network is employed to generate extended training samples from multiclass cell images, with the aim of enhancing the robustness of the resulting model. We aim to optimize plasmodium classification to achieve an exact balance of high accuracy and low resource consumption. A comprehensive comparison of the transformer models to the state-of-the-art methods proves their efficiency in the classification of malaria parasite through thin blood smear microscopic images. Based on our findings, the Swin Transformer model and MobileVit outperform the baseline architectures in terms of precision, recall, F1-score, specificity, and FPR on test set (the data was divided into train: validation: test splits). It is evident that the Swin Transformer achieves superior detection performance (up to 99.8% accuracy), while MobileViT demonstrates lower memory usage and shorter inference times. High accuracy empowers healthcare professionals to conduct precise diagnoses, while low memory usage and short inference times enable the deployment of predictive models on edge devices with limited computational and memory resources. Nature Publishing Group UK 2023-10-10 /pmc/articles/PMC10564789/ /pubmed/37816938 http://dx.doi.org/10.1038/s41598-023-44297-y Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Tan, Dianhuan
Liang, Xianghui
Multiclass malaria parasite recognition based on transformer models and a generative adversarial network
title Multiclass malaria parasite recognition based on transformer models and a generative adversarial network
title_full Multiclass malaria parasite recognition based on transformer models and a generative adversarial network
title_fullStr Multiclass malaria parasite recognition based on transformer models and a generative adversarial network
title_full_unstemmed Multiclass malaria parasite recognition based on transformer models and a generative adversarial network
title_short Multiclass malaria parasite recognition based on transformer models and a generative adversarial network
title_sort multiclass malaria parasite recognition based on transformer models and a generative adversarial network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10564789/
https://www.ncbi.nlm.nih.gov/pubmed/37816938
http://dx.doi.org/10.1038/s41598-023-44297-y
work_keys_str_mv AT tandianhuan multiclassmalariaparasiterecognitionbasedontransformermodelsandagenerativeadversarialnetwork
AT liangxianghui multiclassmalariaparasiterecognitionbasedontransformermodelsandagenerativeadversarialnetwork