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http://dx.doi.org/10.9717/kmms.2022.25.3.451

Performance analysis of deep learning-based automatic classification of upper endoscopic images according to data construction  

Seo, Jeong Min (Dept. of Medicine, Gachon University College of Medicine)
Lim, Sang Heon (Dept. of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University)
Kim, Yung Jae (Dept. of Biomedical Engineering,, College of IT Convergence, Gachon University)
Chung, Jun Won (Department of Internal Medicine, Gachon University Gil Medical Center)
Kim, Kwang Gi (Department of Biomedical Engineering Medical Center, Gachon University, College of Medicine)
Publication Information
Journal of Korea Multimedia Society / v.25, no.3, 2022 , pp. 451-460 More about this Journal
Abstract
Recently, several deep learning studies have been reported to automatically identify the location of diagnostic devices using endoscopic data. In previous studies, there was no design to determine whether the configuration of the dataset resulted in differences in the accuracy in which artificial intelligence models perform image classification. Studies that are based on large amounts of data are likely to have different results depending on the composition of the dataset or its proportion. In this study, we intended to determine the existence and extent of accuracy according to the composition of the dataset by compiling it into three main types using larynx, esophagus, gastroscopy, and laryngeal endoscopy images.
Keywords
Image Classification Model; Artificial Intelligence; Deep Learning; Dataset; Accuracy; Upper Endoscopy;
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