Author:
Zhou Haiying,Yu Xiangyu,Alhaskawi Ahmad,Dong Yanzhao,Wang Zewei,Jin Qianjun,Hu Xianliang,Liu Zongyu,Kota Vishnu Goutham,Abdulla Mohamed Hasan Abdulla Hasan,Ezzi Sohaib Hasan Abdullah,Qi Binjie,Li Juan,Wang Bixian,Fang Jianyong,Lu Hui
Abstract
AbstractAs the demand for health grows, the increase in medical waste generation is gradually outstripping the load. In this paper, we propose a deep learning approach for identification and classification of medical waste. Deep learning is currently the most popular technique in image classification, but its need for large amounts of data limits its usage. In this scenario, we propose a deep learning-based classification method, in which ResNeXt is a suitable deep neural network for practical implementation, followed by transfer learning methods to improve classification results. We pay special attention to the problem of medical waste classification, which needs to be solved urgently in the current environmental protection context. We applied the technique to 3480 images and succeeded in correctly identifying 8 kinds of medical waste with an accuracy of 97.2%; the average F1-score of five-fold cross-validation was 97.2%. This study provided a deep learning-based method for automatic detection and classification of 8 kinds of medical waste with high accuracy and average precision. We believe that the power of artificial intelligence could be harnessed in products that would facilitate medical waste classification and could become widely available throughout China.
Funder
National Natural Science Foundation of China
Shanghai "Science and Technology Innovation Action Plan" Enterprise International Science and Technology Cooperation Project
Alibaba Youth Studio Project
Publisher
Springer Science and Business Media LLC
Cited by
21 articles.
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