TY - GEN
T1 - Human Stools Classification for Gastrointestinal Health based on an Improved ResNet18 Model with Dual Attention Mechanism
AU - Zhang, Jing
AU - Wen, Tao
AU - He, Tao
AU - Wang, Xiangzhou
AU - Hao, Ruqian
AU - Liu, Juanxiu
AU - Du, Xiaohui
AU - Liu, Lin
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The human stools are directly related to the health of human gastrointestinal function. Preliminary classification of the shape and colour of stools can diagnose the health status of peoples, therefore automatic recognition of stools is the current development direction of smart toilets. Due to the difficulty in identification with complex image content, this paper proposed a convolutional neural network called StoolNet to solve the current challenges. The architecture of StoolNet is based on ResNet and contains two output branches which perform colour and shape recognition, respectively. To improve the recognition performance, the dual attention mechanism was introduced into feature extraction stage. The accuracy value of our proposed model could achieve 99.7% and 94.4% for color and shape recognition on our test set, respectively. Experimental results show that, compared with other stool classification algorithms, our method possesses better capability of category discrimination on real dataset.
AB - The human stools are directly related to the health of human gastrointestinal function. Preliminary classification of the shape and colour of stools can diagnose the health status of peoples, therefore automatic recognition of stools is the current development direction of smart toilets. Due to the difficulty in identification with complex image content, this paper proposed a convolutional neural network called StoolNet to solve the current challenges. The architecture of StoolNet is based on ResNet and contains two output branches which perform colour and shape recognition, respectively. To improve the recognition performance, the dual attention mechanism was introduced into feature extraction stage. The accuracy value of our proposed model could achieve 99.7% and 94.4% for color and shape recognition on our test set, respectively. Experimental results show that, compared with other stool classification algorithms, our method possesses better capability of category discrimination on real dataset.
UR - http://www.scopus.com/inward/record.url?scp=85137804852&partnerID=8YFLogxK
U2 - 10.1109/CVPRW56347.2022.00227
DO - 10.1109/CVPRW56347.2022.00227
M3 - Conference contribution
AN - SCOPUS:85137804852
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 2095
EP - 2102
BT - Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
PB - IEEE Computer Society
T2 - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
Y2 - 19 June 2022 through 20 June 2022
ER -
