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KMID : 1137820220430020102
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의공학회지
2022 Volume.43 No. 2 p.102 ~ p.108
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Development of Deep Learning-based Clinical Decision Supporting Technique for Laryngeal Disease using Endoscopic Images
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Jung In-Ho
Hwang Young-Jun
Sung Eui-Suk
Nam Kyoung-Won
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Abstract
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Purpose: To propose a deep learning-based clinical decision support technique for laryngeal disease on epiglottis, tongue and vocal cords.
Materials and Methods: A total of 873 laryngeal endoscopic images were acquired from the PACS database of Pusan N ational University Yangsan Hospital. and VGG16 model was applied with transfer learning and fine-tuning.
Results: The values of precision, recall, accuracy and F1-score for test dataset were 0.94, 0.97, 0.95 and 0.95 for epiglottis images, 0.91, 1.00, 0.95 and 0.95 for tongue images, and 0.90, 0.64, 0.73 and 0.75 for vocal cord images, respectively.
Conclusion: Experimental results demonstrated that the proposed model have a potential as a tool for decision-supporting of otolaryngologist during manual inspection of laryngeal endoscopic images.
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KEYWORD
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Clinical decision support, Endoscopic image, Deep learning, VGG16, Epiglottis, Tongue, vocal cords
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