Improvement of EfficientNet in medical waste classification
DOI:
https://doi.org/10.61173/dzxz2j87Keywords:
EfficientNet, Medical waste classification, Deep learning, Significant advantageAbstract
In recent years, medical waste has gained attention due to its hazardous nature, complexity, and high cost of manual sorting and management. Therefore, it is crucial to develop classification systems that are accurate and efficient. This study analyzes various deep learning models for medical waste classification, compares their accuracies in image recognition, and provides an in-depth analysis of EfficientNet, a classification model that is well-suited to handle large amounts of waste mixing. EfficientNet’s superior performance can be adapted to numerous potential scenarios in medical waste, and its improved performance is also very promising in the field of medical waste classification. The data demonstrate its significant advantages over other models, indicating broad application prospects and economic benefits.
References
[1] Xizheng Wang, Caihong Liu, Junyan Li, et al. A survey on medical waste management in primary healthcare organizations in China [J]. China Infection Control Magazine, 2016, 15(09):698-701.
[2] Yuanyuan Zhang, Chunhe Zhu. Problems and countermeasures of medical waste management under the background of “Internet + Nursing Service”[J]. China Nursing Management, 2019, 19(07): 972-74.
[3] Bruno A, Caudai C, Leone G R, et al. Medical Waste Sorting: a computer vision approach for assisted primary sorting[J]. ar**v preprint ar**v:2303.04720, 2023.
[4] Adedeji O, Wang Z. Intelligent Waste Classification System Using Deep Learning Convolutional Neural Network. Procedia Manufacturing. 2019; 35:607– 612.
[5] Jiale Chu, Nan Wan, Xiaolong Ye, et al. Design and realization of medical waste classification system[J]. Fujian Computer,2023,39(12): 112-115.DOI: 10.16707/j.cnki. fjpc.2023.12.024.
[6] Zhou Zhehua. Design of Medical Waste Identification and Sorting Dumpster [D]. Donghua University, 2021.DOI: 10.27012/d.cnki.gdhuu.2021.000288.
[7] Bian, Xiaoxiao, et al. “Medical waste classification system based on OpenCV and ssd-mobilenet for 5g.” 2021 IEEE wireless communications and networking conference workshops (WCNCW). IEEE, 2021.
[8] Mythili, T., and A. Anbarasi. “Enhanced segmentation network with deep learning for biomedical waste classification.” Indian Journal of science and technology 14.2 (2021).
[9] Vo, Anh H., Minh Thanh Vo, and Tuong Le. “A novel framework for trash classification using deep transfer learning.” IEEE Access 7 (2019): 178631-178639.
[10] Zhou, H., Yu, X., Alhaskawi, A. et al. A deep learning approach for medical waste classification. Sci Rep 12, 2159 (2022). https://doi.org/10.1038/s41598-022-06146-2
[11] Yupeng Mou. Applied Research on Medical Waste Classification Based on Deep Learning [D]. Dalian Jiao tong University, 2022.DOI: 10.26990/d.cnki.gsltc.2022.000512.
[12] Tan, Mingxing, and Quoc Le. “Efficientnet: Rethinking model scaling for convolutional neural networks.” International conference on machine learning. PMLR, 2019.
[13] Tan, Mingxing, and Quoc Le. “Efficientnetv2: Smaller models and faster training.” International conference on machine learning. PMLR, 2021.
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