Classification of Domestic Refuse in Medical Institutions Based on Transfer Learning and Convolutional Neural Network
- Dequan Guo1, Qiao Yang2, Yu-Dong Zhang3, Tao Jiang1, Hanbing Yan1,*
1
School of Control Engineering, Chengdu University of Information Technology, Chengdu, 610225, China
2 College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
3 Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK
* Corresponding Author: Hanbing Yan. Email:
(This article belongs to this Special Issue: Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
2 College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
3 Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK
* Corresponding Author: Hanbing Yan. Email:
(This article belongs to this Special Issue: Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Received 01 September 2020; Accepted 01 February 2021; Issue published 19 April 2021
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.
Keywords
Domestic refuse; image classification deep learning; transfer learning; convolutional neural network
Cite This Article
Guo, D., Yang, Q., Zhang, Y., Jiang, T., Yan, H. (2021). Classification of Domestic Refuse in Medical Institutions Based on Transfer Learning and Convolutional Neural Network. CMES-Computer Modeling in Engineering & Sciences, 127(2), 599–620.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.