首页|MedBin: A lightweight End-to-End model-based method for medical waste management
MedBin: A lightweight End-to-End model-based method for medical waste management
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NETL
NSTL
Elsevier
The surge in medical waste has highlighted the urgent need for cost-effective and advanced management solutions. In this paper, a novel medical waste management approach, "MedBin," is proposed for automated sorting, reusing, and recycling. A comprehensive medical waste dataset, "MedBin-Dataset" is established, comprising 2,119 original images spanning 36 categories, with samples captured in various backgrounds. The lightweight "MedBin-Net" model is introduced to enable detection and instance segmentation of medical waste, enhancing waste recognition capabilities. Experimental results demonstrate the effectiveness of the proposed approach, achieving an average precision of 0.91, recall of 0.97, and F1-score of 0.94 across all categories with just 2.51 M parameters (where M stands for million, i.e., 2.51 million parameters), 5.20G FLOPs (where G stands for billion, i.e., 5.20 billion floating-point operations per second), and 0.60 ms inference time. Additionally, the proposed method includes a World Health Organization (WHO) Guideline-Based Classifier that categorizes detected waste into 5 types, each with a corresponding disposal method, following WHO medical waste classification standards. The proposed method, along with the dedicated dataset, offers a promising solution that supports sustainable medical waste management and other related applications.
Medical waste managementLightweight neural networkDeep learningObject detectionInstance segmentation
Xiazhen Xu、Chenyang Wang、Qiufeng Yi、Jiaqi Ye、Xiangfei Kong、Shazad Q Ashraf、Karl D. Dearn、Amir M. Hajiyavand
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Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK
Queen Elizabeth Hospital Mindelsohn Way, Birmingham B15 2GW, UK
NETL
NSTL
Elsevier
