A deep learning approach for medical waste classification

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; Lü, Hui

doi:10.1038/s41598-022-06146-2

Cited by 33 publications

(10 citation statements)

References 22 publications

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“…Using the articles collected in this manner, effective and efficient technologies that can be used for sorting mixed industrial waste were investigated in this study. From these investigations, domain-specific sorting approaches for underwater [40], [41], [42], [43], floating [44], [45], [46], space [47], [48], nuclear [49], [50], [51], [52], [53], [54], [55], and biomedical [56], [57] waste were excluded. Rather, Municipal and CNI [58] waste, along with ND, CND [36],…”

Section: B Search and Collection Strategymentioning

confidence: 99%

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Kiyokawa

Takamatsu

Koyanaka

2024

IEEE Trans. Automat. Sci. Eng.

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To achieve recycling of mixed industrial waste toward an advanced sustainable society, waste sorting automation through robots is crucial and urgent. For this purpose, a robot is required to recognize the category, shape, pose, and condition of different waste items and manipulate them according to the category to be sorted. This survey considers three potential difficulties in the sorting automation: 1) End-effector: to robustly grasp and manipulate different waste items with dirt and deformations; 2) Sensor: to recognize the category, shape, and pose of existing objects to be manipulated and the wet and dirty conditions of their surfaces; and 3) Planner: to generate feasible and efficient sequences and trajectories. This survey includes 76 references to studies related to automatic waste sorting and 159 references to worldwide waste recycling attempts. This pioneering investigation reveals the possibility and limitations of conventional systems; thus, providing insights on open issues and potential technologies to achieve a robot-incorporated sorter for the chaotic mixed waste is one of its contributions. This paper further presents a system design policy for readers and discusses future advanced sorters, thereby contributing to the field of robotics and automation.Note to Practitioners-Most automated sorting systems operate for limited target waste items. This study is motivated by the automation of mixed industrial waste treatment facilities using advanced robotic sorters. Emerging advances and increasing functionalities of robot system components will widen system applicability and increase use cases in the chaotic mixed industrial waste domain. This paper surveys the research conducted to date, discusses open issues and potential approaches, and presents user guides that provide practitioners with a system design policy. The user guides created according to the strengths and weaknesses of each system configuration provide future researchers and developers with a useful a priori design policy that has been thus far validated on efficiency, quality, productivity, and reliability. A question-and-answer style guide Manuscript

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Section: B Search and Collection Strategymentioning

confidence: 99%

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Kiyokawa

Takamatsu

Koyanaka

2024

IEEE Trans. Automat. Sci. Eng.

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“…Lemma 3. In the PBE of the supply chain Cournot model with asymmetric information on the technology upgrade of machine learning, there must exist a positive amount of technology upgrade for chain 2, and it is given by equation (29).…”

Section: The Case Of Symmetric Informationmentioning

confidence: 99%

Green supply chain transformation and emission reduction based on machine learning

Zuo

2023

Science Progress

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Artificial intelligence techniques provide more possibilities for supply chain transformations in the face of global warming and environmental degradation. This study examines the Cournot game model of two competing supply chains with various carbon emission technologies as well as the possibility of upgrading machine learning technology. The investment risk of a supply chain's technology upgrade is either symmetric or asymmetric information. In the case of symmetric information, results show that the machine learning technology upgrade risk does not affect the market equilibrium outcomes of the duopoly model. However, in the case of asymmetric information, technology upgrade risk is vital in determining the quantities and prices of competition equilibrium. To achieve the goal of green supply chain transformation, the government should provide more technology and financial support to traditional supply chains to upgrade their machine learning technology on carbon emissions.

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“…The dataset in any case seems to be small and not sufficiently representative. A more comprehensive work is presented in [13], where they applied a fine-tuned version of ResNext [14] on a dataset of 3480 images and succeeded in correctly identifying eight kinds of medical waste with an accuracy of 97.2%. Unfortunately, the dataset has not been disclosed.…”

Section: Related Workmentioning

confidence: 99%

Medical Waste Sorting: a computer vision approach for assisted primary sorting

Bruno¹,

Caudai²,

Leone³

et al. 2023

Preprint

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Medical waste, i.e. waste produced during medical activities in hospitals, clinics and laboratories, represents hazardous waste whose management involves special care and high costs. However, this kind of waste contains a significant fraction of highly valued materials that can enter a circular economy process. To this end, in this paper, we propose a computer vision approach for assisting in the primary sorting of medical waste. The feasibility of our approach is demonstrated on a representative dataset we collected and made available to the community, with which we have trained a model that achieves 100% accuracy, and a new dataset on which the trained model exhibits good generalization.

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A deep learning approach for medical waste classification

Cited by 33 publications

References 22 publications

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Green supply chain transformation and emission reduction based on machine learning

Medical Waste Sorting: a computer vision approach for assisted primary sorting

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