A deep learning approach for medical waste classification
Haiying Zhou, Xiangyu Yu, Ahmad Alhaskawi, Yanzhao Dong, Zewei Wang, Qianjun Jin, Xianliang Hu, Zongyu Liu, Vishnu Goutham Kota, Mohamed Hasan Abdulla Hasan Abdulla, Sohaib Hasan Abdullah Ezzi, Binjie Qi, Juan Li, Bixian Wang, Jianyong Fang, Hui Lü
Abstract
As 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.