TY - GEN
T1 - Bleeding Region Segmentation in Wireless Capsule Endoscopy Images by a Deep Learning Model
T2 - 2022 International Conference on Decision Aid Sciences and Applications, DASA 2022
AU - Duangchai, Ratchaneekorn
AU - Toonmana, Chanakarn
AU - Numpacharoen, Kawee
AU - Wiwatwattana, Nuwee
AU - Charoen, Amber
AU - Charoenpong, Theekapun
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - A common symptom in gastrointestinal tract is gastrointestinal bleeding, which can lead to serious conditions. The neural network technique is developed to segment the bleeding area in images from Wireless Capsule Endoscope. Initial variable is also importance for performance of the algorithm. In this paper, a bleeding segmentation method using a deep neural network algorithm is proposed. Variables which effect on performance of the deep learning technique in training process are studied. Initial learn rate is varied from 0.009, 0.006, 0.003, 0.06, and 0.09. Epoch is varied from 1,000 to 10,000 iterations. To evaluate the performance of segmentation method, 48 Image in KID dataset were used in the experiment. DICE rate of is 90.82%, and 69.91% for training data and test data, respectively. Based on the experiment, initial learning rate, and number of epoch effects to the performance of the method.
AB - A common symptom in gastrointestinal tract is gastrointestinal bleeding, which can lead to serious conditions. The neural network technique is developed to segment the bleeding area in images from Wireless Capsule Endoscope. Initial variable is also importance for performance of the algorithm. In this paper, a bleeding segmentation method using a deep neural network algorithm is proposed. Variables which effect on performance of the deep learning technique in training process are studied. Initial learn rate is varied from 0.009, 0.006, 0.003, 0.06, and 0.09. Epoch is varied from 1,000 to 10,000 iterations. To evaluate the performance of segmentation method, 48 Image in KID dataset were used in the experiment. DICE rate of is 90.82%, and 69.91% for training data and test data, respectively. Based on the experiment, initial learning rate, and number of epoch effects to the performance of the method.
KW - DICE Rate
KW - Gastrointestinal bleeding
KW - Segmentation
KW - Wireless Capsule Endoscope image
UR - http://www.scopus.com/inward/record.url?scp=85130202552&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85130202552&partnerID=8YFLogxK
U2 - 10.1109/DASA54658.2022.9765175
DO - 10.1109/DASA54658.2022.9765175
M3 - Conference contribution
AN - SCOPUS:85130202552
T3 - 2022 International Conference on Decision Aid Sciences and Applications, DASA 2022
SP - 1460
EP - 1463
BT - 2022 International Conference on Decision Aid Sciences and Applications, DASA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 March 2022 through 25 March 2022
ER -