Transfer Learning for <named-content content-type="genus-species">Toxoplasma gondii</named-content> Recognition

ABSTRACT Toxoplasma gondii, one of the world’s most common parasites, can infect all types of warm-blooded animals, including one-third of the world’s human population. Most current routine diagnostic methods are costly, time-consuming, and labor-intensive. Although T. gondii can be directly observe...

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Autores principales:	Sen Li, Aijia Li, Diego Alejandro Molina Lara, Jorge Enrique Gómez Marín, Mario Juhas, Yang Zhang
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	Toxoplasma gondii artificial intelligence transfer learning microscopic image banana shaped Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/ae606ac394454b4e85455510d65740c1
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spelling	oai:doaj.org-article:ae606ac394454b4e85455510d65740c12021-12-02T18:25:16ZTransfer Learning for <named-content content-type="genus-species">Toxoplasma gondii</named-content> Recognition10.1128/mSystems.00445-192379-5077https://doaj.org/article/ae606ac394454b4e85455510d65740c12020-02-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00445-19https://doaj.org/toc/2379-5077ABSTRACT Toxoplasma gondii, one of the world’s most common parasites, can infect all types of warm-blooded animals, including one-third of the world’s human population. Most current routine diagnostic methods are costly, time-consuming, and labor-intensive. Although T. gondii can be directly observed under the microscope in tissue or spinal fluid samples, this form of identification is difficult and requires well-trained professionals. Nevertheless, the traditional identification of parasites under the microscope is still performed by a large number of laboratories. Novel, efficient, and reliable methods of T. gondii identification are therefore needed, particularly in developing countries. To this end, we developed a novel transfer learning-based microscopic image recognition method for T. gondii identification. This approach employs the fuzzy cycle generative adversarial network (FCGAN) with transfer learning utilizing knowledge gained by parasitologists that Toxoplasma is banana or crescent shaped. Our approach aims to build connections between microscopic and macroscopic associated objects by embedding the fuzzy C-means cluster algorithm into the cycle generative adversarial network (Cycle GAN). Our approach achieves 93.1% and 94.0% detection accuracy for ×400 and ×1,000 Toxoplasma microscopic images, respectively. We showed the high accuracy and effectiveness of our approach in newly collected unlabeled Toxoplasma microscopic images, compared to other currently available deep learning methods. This novel method for Toxoplasma microscopic image recognition will open a new window for developing cost-effective and scalable deep learning-based diagnostic solutions, potentially enabling broader clinical access in developing countries. IMPORTANCE Toxoplasma gondii, one of the world’s most common parasites, can infect all types of warm-blooded animals, including one-third of the world’s human population. Artificial intelligence (AI) could provide accurate and rapid diagnosis in fighting Toxoplasma. So far, none of the previously reported deep learning methods have attempted to explore the advantages of transfer learning for Toxoplasma detection. The knowledge from parasitologists is that the Toxoplasma parasite is generally banana or crescent shaped. Based on this, we built connections between microscopic and macroscopic associated objects by embedding the fuzzy C-means cluster algorithm into the cycle generative adversarial network (Cycle GAN). Our approach achieves high accuracy and effectiveness in ×400 and ×1,000 Toxoplasma microscopic images.Sen LiAijia LiDiego Alejandro Molina LaraJorge Enrique Gómez MarínMario JuhasYang ZhangAmerican Society for MicrobiologyarticleToxoplasma gondiiartificial intelligencetransfer learningmicroscopic imagebanana shapedMicrobiologyQR1-502ENmSystems, Vol 5, Iss 1 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Toxoplasma gondii artificial intelligence transfer learning microscopic image banana shaped Microbiology QR1-502
spellingShingle	Toxoplasma gondii artificial intelligence transfer learning microscopic image banana shaped Microbiology QR1-502 Sen Li Aijia Li Diego Alejandro Molina Lara Jorge Enrique Gómez Marín Mario Juhas Yang Zhang Transfer Learning for <named-content content-type="genus-species">Toxoplasma gondii</named-content> Recognition
description	ABSTRACT Toxoplasma gondii, one of the world’s most common parasites, can infect all types of warm-blooded animals, including one-third of the world’s human population. Most current routine diagnostic methods are costly, time-consuming, and labor-intensive. Although T. gondii can be directly observed under the microscope in tissue or spinal fluid samples, this form of identification is difficult and requires well-trained professionals. Nevertheless, the traditional identification of parasites under the microscope is still performed by a large number of laboratories. Novel, efficient, and reliable methods of T. gondii identification are therefore needed, particularly in developing countries. To this end, we developed a novel transfer learning-based microscopic image recognition method for T. gondii identification. This approach employs the fuzzy cycle generative adversarial network (FCGAN) with transfer learning utilizing knowledge gained by parasitologists that Toxoplasma is banana or crescent shaped. Our approach aims to build connections between microscopic and macroscopic associated objects by embedding the fuzzy C-means cluster algorithm into the cycle generative adversarial network (Cycle GAN). Our approach achieves 93.1% and 94.0% detection accuracy for ×400 and ×1,000 Toxoplasma microscopic images, respectively. We showed the high accuracy and effectiveness of our approach in newly collected unlabeled Toxoplasma microscopic images, compared to other currently available deep learning methods. This novel method for Toxoplasma microscopic image recognition will open a new window for developing cost-effective and scalable deep learning-based diagnostic solutions, potentially enabling broader clinical access in developing countries. IMPORTANCE Toxoplasma gondii, one of the world’s most common parasites, can infect all types of warm-blooded animals, including one-third of the world’s human population. Artificial intelligence (AI) could provide accurate and rapid diagnosis in fighting Toxoplasma. So far, none of the previously reported deep learning methods have attempted to explore the advantages of transfer learning for Toxoplasma detection. The knowledge from parasitologists is that the Toxoplasma parasite is generally banana or crescent shaped. Based on this, we built connections between microscopic and macroscopic associated objects by embedding the fuzzy C-means cluster algorithm into the cycle generative adversarial network (Cycle GAN). Our approach achieves high accuracy and effectiveness in ×400 and ×1,000 Toxoplasma microscopic images.
format	article
author	Sen Li Aijia Li Diego Alejandro Molina Lara Jorge Enrique Gómez Marín Mario Juhas Yang Zhang
author_facet	Sen Li Aijia Li Diego Alejandro Molina Lara Jorge Enrique Gómez Marín Mario Juhas Yang Zhang
author_sort	Sen Li
title	Transfer Learning for <named-content content-type="genus-species">Toxoplasma gondii</named-content> Recognition
title_short	Transfer Learning for <named-content content-type="genus-species">Toxoplasma gondii</named-content> Recognition
title_full	Transfer Learning for <named-content content-type="genus-species">Toxoplasma gondii</named-content> Recognition
title_fullStr	Transfer Learning for <named-content content-type="genus-species">Toxoplasma gondii</named-content> Recognition
title_full_unstemmed	Transfer Learning for <named-content content-type="genus-species">Toxoplasma gondii</named-content> Recognition
title_sort	transfer learning for <named-content content-type="genus-species">toxoplasma gondii</named-content> recognition
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/ae606ac394454b4e85455510d65740c1
work_keys_str_mv	AT senli transferlearningfornamedcontentcontenttypegenusspeciestoxoplasmagondiinamedcontentrecognition AT aijiali transferlearningfornamedcontentcontenttypegenusspeciestoxoplasmagondiinamedcontentrecognition AT diegoalejandromolinalara transferlearningfornamedcontentcontenttypegenusspeciestoxoplasmagondiinamedcontentrecognition AT jorgeenriquegomezmarin transferlearningfornamedcontentcontenttypegenusspeciestoxoplasmagondiinamedcontentrecognition AT mariojuhas transferlearningfornamedcontentcontenttypegenusspeciestoxoplasmagondiinamedcontentrecognition AT yangzhang transferlearningfornamedcontentcontenttypegenusspeciestoxoplasmagondiinamedcontentrecognition
_version_	1718378057470836736

Transfer Learning for <named-content content-type="genus-species">Toxoplasma gondii</named-content> Recognition

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