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The Detection of Esophagitis by Using Back Propagation Network Algorithm  

Seo, Kwang-Wook (Department of Bio-Mechartronic Engineering, SungKyunKwan University)
Min, Byeong-Ro (Department of Bio-Mechartronic Engineering, SungKyunKwan University)
Lee, Dae-Weon (Department of Bio-Mechartronic Engineering, SungKyunKwan University)
Publication Information
Journal of Mechanical Science and Technology / v.20, no.11, 2006 , pp. 1873-1880 More about this Journal
Abstract
The results of this study suggest the use of a Back Propagation Network (BPN) algorithm for the detection of esophageal erosions or abnormalities - which are the important signs of esophagitis - in the analysis of the color and textural aspects of clinical images obtained by endoscopy. The authors have investigated the optimization of the learning condition by the number of neurons in the hidden layer within the structure of the neural network. By optimizing learning parameters, we learned and have validated esophageal erosion images and/or ulcers functioning as the critical diagnostic criteria for esophagitis and associated abnormalities. Validation was established by using twenty clinical images. The success rates for detection of esophagitis during calibration and during validation were 97.91% and 96.83%, respectively.
Keywords
Back Propagation Network; Endoscopy; Esophagitis; Texture;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
Times Cited By Web Of Science : 1  (Related Records In Web of Science)
Times Cited By SCOPUS : 1
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