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Paper Abstract and Keywords
Presentation 2024-03-05 13:42
Development of a Diagnostic Support System for Intranasal Disease
Kaho Ukai, Youngha Chang, Nobuhiko Mukai (TCU), Kojiro Hirano, Kouzou Murakami (SUSM/SUH)
Abstract (in Japanese) (See Japanese page) 
(in English) In this research, a support system has been developed to diagnose whether an endoscopic image shows "severely abnormal nasal cavity (Symptom+)" or "not severely abnormal nasal cavity (Symptom-)". ResNet50 and VGG16 are employed as the deep learning models, and fine-tuning is performed with endoscope images of the nasal cavity after pre-training with ImageNet. The average diagnostic accuracy of stratified 4-fold cross-validation was about 80%, while the recall rate of Symptom+ was about 60%. In the future, we plan to improve the recall rate for practical use.
Keyword (in Japanese) (See Japanese page) 
(in English) Intra nasal disease / Endoscope images / Deep learning / Fine tuning / Computer-aided diagnosis / / /  
Reference Info. ITE Tech. Rep., vol. 48, no. 8, AIT2024-70, pp. 135-138, March 2024.
Paper # AIT2024-70 
Date of Issue 2024-02-27 (AIT) 
ISSN Online edition: ISSN 2424-1970
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Conference Information
Committee AIT IIEEJ AS CG-ARTS  
Conference Date 2024-03-05 - 2024-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Tokyo University of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Expressive Japan 2024 
Paper Information
Registration To AIT 
Conference Code 2024-03-AIT-IIEEJ-AS-ARTS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Development of a Diagnostic Support System for Intranasal Disease 
Sub Title (in English)  
Keyword(1) Intra nasal disease  
Keyword(2) Endoscope images  
Keyword(3) Deep learning  
Keyword(4) Fine tuning  
Keyword(5) Computer-aided diagnosis  
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Keyword(7)  
Keyword(8)  
1st Author's Name Kaho Ukai  
1st Author's Affiliation Tokyo City University (TCU)
2nd Author's Name Youngha Chang  
2nd Author's Affiliation Tokyo City University (TCU)
3rd Author's Name Nobuhiko Mukai  
3rd Author's Affiliation Tokyo City University (TCU)
4th Author's Name Kojiro Hirano  
4th Author's Affiliation Showa University School of Medicine/Showa University Hospital (SUSM/SUH)
5th Author's Name Kouzou Murakami  
5th Author's Affiliation Showa University School of Medicine/Showa University Hospital (SUSM/SUH)
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Speaker Author-1 
Date Time 2024-03-05 13:42:00 
Presentation Time 12 minutes 
Registration for AIT 
Paper # AIT2024-70 
Volume (vol) vol.48 
Number (no) no.8 
Page pp.135-138 
#Pages
Date of Issue 2024-02-27 (AIT) 


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