TY - JOUR
T1 - Classification of Pancreatic Cystic Lesions Using ResNet Deep Learning Network in Confocal Laser Endomicroscopy Videos
AU - Angelina, Clara Lavita
AU - Pan, Chien Ming
AU - Lee, Tsung Chun
AU - Han, Ming Lun
AU - Kongkam, Pradermchai
AU - Wang, Hsiu Po
AU - Chang, Chuan Yu
AU - Chang, Hsuan Ting
N1 - Publisher Copyright:
© 2023 The Authors. Published by Elsevier B.V.
PY - 2024
Y1 - 2024
N2 - Accurate classification of pancreatic cystic lesions is crucial to differentiate mucinous lesions of malignant potential. We utilized the ResNet-50 and ResNet-101 network to develop a model for classification of the pancreatic cystic lesions. A total of 50 videos, 13,425 images, from five types of pancreatic cystic lesions and utilize the image rotation and contrast reversal scheme for the training. We adopt a contrast limited adaptive histogram equalization method onto the test video. Our method can automatically classify the feature type and record the prediction results frame by frame. The method has been evaluated on 18 test videos and achieves an accuracy 94% overall.
AB - Accurate classification of pancreatic cystic lesions is crucial to differentiate mucinous lesions of malignant potential. We utilized the ResNet-50 and ResNet-101 network to develop a model for classification of the pancreatic cystic lesions. A total of 50 videos, 13,425 images, from five types of pancreatic cystic lesions and utilize the image rotation and contrast reversal scheme for the training. We adopt a contrast limited adaptive histogram equalization method onto the test video. Our method can automatically classify the feature type and record the prediction results frame by frame. The method has been evaluated on 18 test videos and achieves an accuracy 94% overall.
KW - Contrast Limited Adaptive Histogram Equalization
KW - Contrast Reversal
KW - Deep Learning
KW - Pancreatic Cystic Lesions
KW - ResNet-101
KW - ResNet-50
UR - http://www.scopus.com/inward/record.url?scp=85193200958&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85193200958&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2024.03.015
DO - 10.1016/j.procs.2024.03.015
M3 - Conference article
AN - SCOPUS:85193200958
SN - 1877-0509
VL - 234
SP - 357
EP - 363
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 7th Information Systems International Conference, ISICO 2023
Y2 - 26 July 2023 through 28 July 2023
ER -