@Article{cmes.2021.014119,
AUTHOR = {Dequan Guo, Qiao Yang, Yu-Dong Zhang, Tao Jiang, Hanbing Yan},
TITLE = {Classification of Domestic Refuse in Medical Institutions Based on Transfer Learning and Convolutional Neural Network},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {127},
YEAR = {2021},
NUMBER = {2},
PAGES = {599--620},
URL = {http://www.techscience.com/CMES/v127n2/42224},
ISSN = {1526-1506},
ABSTRACT = {The problem of domestic refuse is becoming more and more serious with the use of all kinds of equipment in
medical institutions. This matter arouses people’s attention. Traditional artificial waste classification is subjective
and cannot be put accurately; moreover, the working environment of sorting is poor and the efficiency is low.
Therefore, automated and effective sorting is needed. In view of the current development of deep learning, it can
provide a good auxiliary role for classification and realize automatic classification. In this paper, the ResNet-50
convolutional neural network based on the transfer learning method is applied to design the image classifier to
obtain the domestic refuse classification with high accuracy. By comparing the method designed in this paper with
back propagation neural network and convolutional neural network , it is concluded that the CNN based on transfer
learning method applied in this paper with higher accuracy rate and lower false detection rate. Further, under the
shortage situation of data samples, the method with transfer learning and ResNet-50 training model is effective to
improve the accuracy of image classification.},
DOI = {10.32604/cmes.2021.014119}
}