Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/101231 https://doi.org/10.1109/ACCESS.2020.3005687 |
Resumo: | The relationship between face and disease has been discussed from thousands years ago, which leads to the occurrence of facial diagnosis. The objective here is to explore the possibility of identifying diseases from uncontrolled 2D face images by deep learning techniques. In this paper, we propose using deep transfer learning from face recognition to perform the computer-aided facial diagnosis on various diseases. In the experiments, we perform the computer-aided facial diagnosis on single (beta-thalassemia) and multiple diseases (beta-thalassemia, hyperthyroidism, Down syndrome, and leprosy) with a relatively small dataset. The overall top-1 accuracy by deep transfer learning from face recognition can reach over 90% which outperforms the performance of both traditional machine learning methods and clinicians in the experiments. In practical, collecting disease-speci c face images is complex, expensive and time consuming, and imposes ethical limitations due to personal data treatment. Therefore, the datasets of facial diagnosis related researches are private and generally small comparing with the ones of other machine learning application areas. The success of deep transfer learning applications in the facial diagnosis with a small dataset could provide a low-cost and noninvasive way for disease screening and detection. |
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Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial DiagnosisFacial diagnosisdeep transfer learning (DTL)face recognitionbeta-thalassemiahyperthyroidismdown syndromeleprosyThe relationship between face and disease has been discussed from thousands years ago, which leads to the occurrence of facial diagnosis. The objective here is to explore the possibility of identifying diseases from uncontrolled 2D face images by deep learning techniques. In this paper, we propose using deep transfer learning from face recognition to perform the computer-aided facial diagnosis on various diseases. In the experiments, we perform the computer-aided facial diagnosis on single (beta-thalassemia) and multiple diseases (beta-thalassemia, hyperthyroidism, Down syndrome, and leprosy) with a relatively small dataset. The overall top-1 accuracy by deep transfer learning from face recognition can reach over 90% which outperforms the performance of both traditional machine learning methods and clinicians in the experiments. In practical, collecting disease-speci c face images is complex, expensive and time consuming, and imposes ethical limitations due to personal data treatment. Therefore, the datasets of facial diagnosis related researches are private and generally small comparing with the ones of other machine learning application areas. The success of deep transfer learning applications in the facial diagnosis with a small dataset could provide a low-cost and noninvasive way for disease screening and detection.2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/101231http://hdl.handle.net/10316/101231https://doi.org/10.1109/ACCESS.2020.3005687eng2169-3536Jin, BoCruz, LeandroGonçalves, Nunoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-08-17T23:02:28Zoai:estudogeral.uc.pt:10316/101231Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:18:28.551218Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis |
title |
Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis |
spellingShingle |
Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis Jin, Bo Facial diagnosis deep transfer learning (DTL) face recognition beta-thalassemia hyperthyroidism down syndrome leprosy |
title_short |
Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis |
title_full |
Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis |
title_fullStr |
Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis |
title_full_unstemmed |
Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis |
title_sort |
Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis |
author |
Jin, Bo |
author_facet |
Jin, Bo Cruz, Leandro Gonçalves, Nuno |
author_role |
author |
author2 |
Cruz, Leandro Gonçalves, Nuno |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Jin, Bo Cruz, Leandro Gonçalves, Nuno |
dc.subject.por.fl_str_mv |
Facial diagnosis deep transfer learning (DTL) face recognition beta-thalassemia hyperthyroidism down syndrome leprosy |
topic |
Facial diagnosis deep transfer learning (DTL) face recognition beta-thalassemia hyperthyroidism down syndrome leprosy |
description |
The relationship between face and disease has been discussed from thousands years ago, which leads to the occurrence of facial diagnosis. The objective here is to explore the possibility of identifying diseases from uncontrolled 2D face images by deep learning techniques. In this paper, we propose using deep transfer learning from face recognition to perform the computer-aided facial diagnosis on various diseases. In the experiments, we perform the computer-aided facial diagnosis on single (beta-thalassemia) and multiple diseases (beta-thalassemia, hyperthyroidism, Down syndrome, and leprosy) with a relatively small dataset. The overall top-1 accuracy by deep transfer learning from face recognition can reach over 90% which outperforms the performance of both traditional machine learning methods and clinicians in the experiments. In practical, collecting disease-speci c face images is complex, expensive and time consuming, and imposes ethical limitations due to personal data treatment. Therefore, the datasets of facial diagnosis related researches are private and generally small comparing with the ones of other machine learning application areas. The success of deep transfer learning applications in the facial diagnosis with a small dataset could provide a low-cost and noninvasive way for disease screening and detection. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/101231 http://hdl.handle.net/10316/101231 https://doi.org/10.1109/ACCESS.2020.3005687 |
url |
http://hdl.handle.net/10316/101231 https://doi.org/10.1109/ACCESS.2020.3005687 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2169-3536 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134079161991168 |