TY - GEN
T1 - Classification of ulcerative colitis severity in colonoscopy videos using CNN
AU - Alammari, Ali
AU - Islam, A. B.M.Rezbaul
AU - Oh, Jung Hwan
AU - Tavanapong, Wallapak
AU - Wong, Johnny
AU - De Groen, Piet C.
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/10/9
Y1 - 2017/10/9
N2 - Ulcerative Colitis (UC) is a chronic inflammatory disease characterized by periods of relapses and remissions affecting more than 500,000 people in the United States. The therapeutic goals of UC are to first induce and then maintain disease remission. However, it is very difficult to evaluate the severity of UC objectively because of non-uniform nature of symptoms and large variations in their patterns. To address this, we already developed an approach using the image textures in our previous work. But, we found that it could not handle larger number of variations in their patterns. In this paper, we propose a different approach using CNN (Convolutional Neural Network) to measure and classify objectively the severity of UC presented in optical colonoscopy video frames. We call the proposed approach using CNN as Ulcerative Colitis Severity CNN (UCS-CNN) which utilizes endoscopic domain knowledge and convolutional neural network to classify different UC severity of colonoscopy images. The experimental results show that the proposed UCS-CNN can evaluate the severity of UC reasonably.
AB - Ulcerative Colitis (UC) is a chronic inflammatory disease characterized by periods of relapses and remissions affecting more than 500,000 people in the United States. The therapeutic goals of UC are to first induce and then maintain disease remission. However, it is very difficult to evaluate the severity of UC objectively because of non-uniform nature of symptoms and large variations in their patterns. To address this, we already developed an approach using the image textures in our previous work. But, we found that it could not handle larger number of variations in their patterns. In this paper, we propose a different approach using CNN (Convolutional Neural Network) to measure and classify objectively the severity of UC presented in optical colonoscopy video frames. We call the proposed approach using CNN as Ulcerative Colitis Severity CNN (UCS-CNN) which utilizes endoscopic domain knowledge and convolutional neural network to classify different UC severity of colonoscopy images. The experimental results show that the proposed UCS-CNN can evaluate the severity of UC reasonably.
KW - Convolutional neural network
KW - Medical image classification
KW - Medical video processing
KW - Ulcerative colitis severity
UR - http://www.scopus.com/inward/record.url?scp=85045668303&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045668303&partnerID=8YFLogxK
U2 - 10.1145/3149572.3149613
DO - 10.1145/3149572.3149613
M3 - Conference contribution
AN - SCOPUS:85045668303
T3 - ACM International Conference Proceeding Series
SP - 139
EP - 144
BT - 2017 9th International Conference on Information Management and Engineering, ICIME 2017
PB - Association for Computing Machinery
T2 - 9th International Conference on Information Management and Engineering, ICIME 2017
Y2 - 9 October 2017 through 11 October 2017
ER -