A deep learning approach for medical waste classification

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.

Figure 1

Examples of the medical waste. (a) Gauze, (b) Gloves, (c) Infusion bags and bottles, (d) Infusion apparatus and syringe, (e) Syringe needles, (f) Tweezers.

Figure 2

Deep MW: a overview of the deep learning framework.

Figure 3

History curves for train accuracy (blue line) and valid accuracy (yellow line).

Figure 4

History curves for train loss(blue line) and valid loss(yellow line).

Figure 5

Confusion matrix for the eight categories classification.