Performance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Works
This paper evaluates the performance of modern AI-based object detection models that can be used for object classification and sorting applications. In this case, we focused on the classification of the medical waste for the current global situation which is the medical waste management during the p...
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2025
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my.uniten.dspace-371442025-03-03T15:47:57Z Performance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Works Moktar M.H. Hajjaj S. Mohamed H. Weng L.Y. 57215719975 55812832600 57136356100 26326032700 Image enhancement Medical imaging Object detection Statistical tests Waste management AI algorithms Deep learning Detection models Medical wastes Object classification Object sorting Objects detection Performance Performance reviews Waste sorting Deep learning This paper evaluates the performance of modern AI-based object detection models that can be used for object classification and sorting applications. In this case, we focused on the classification of the medical waste for the current global situation which is the medical waste management during the post-pandemic of Covid-19 phase. A few classification models were used and compared between (1) CNN and ResNet50 and (2) YOLO v3 and YOLO v4. The results also were compared with the previous works that focused on waste classification. The difference between this work is the image dataset, which our work train and test the medical waste (facemask, glove, and syringe), while the previous works focused on general waste such as food, plastic, metal, paper, and others. From 2207 images of the medical waste, CNN and ResNet achieved 89.35 and 85.75% of accuracy, respectively, where it requires more images per class for the training improvement. YOLO v3 and YOLO v4 used 3073 images for training and achieved 84.86 and 89.21% of mean average precision (mAP). Our YOLO v3 mAP is in the average value among the previous works, while YOLO v4 has a higher mAP compared to the YOLO v4 training from other works. The YOLO v4 then was tested in real-time medical waste detection and successfully detected the masks, gloves, and syringe. However, there are still some wrong detections during the real-time detection using the camera, especially with other objects with similar shapes to the medical waste. Further, performance evaluations are required that can be used for medical waste objects and also for other different objects based on the applications. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Final 2025-03-03T07:47:57Z 2025-03-03T07:47:57Z 2024 Conference paper 10.1007/978-981-99-8498-5_40 2-s2.0-85187777137 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187777137&doi=10.1007%2f978-981-99-8498-5_40&partnerID=40&md5=d5b6813ad0986a6435b2ae189906cb47 https://irepository.uniten.edu.my/handle/123456789/37144 845 475 489 Springer Science and Business Media Deutschland GmbH Scopus |
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Image enhancement Medical imaging Object detection Statistical tests Waste management AI algorithms Deep learning Detection models Medical wastes Object classification Object sorting Objects detection Performance Performance reviews Waste sorting Deep learning |
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Image enhancement Medical imaging Object detection Statistical tests Waste management AI algorithms Deep learning Detection models Medical wastes Object classification Object sorting Objects detection Performance Performance reviews Waste sorting Deep learning Moktar M.H. Hajjaj S. Mohamed H. Weng L.Y. Performance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Works |
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This paper evaluates the performance of modern AI-based object detection models that can be used for object classification and sorting applications. In this case, we focused on the classification of the medical waste for the current global situation which is the medical waste management during the post-pandemic of Covid-19 phase. A few classification models were used and compared between (1) CNN and ResNet50 and (2) YOLO v3 and YOLO v4. The results also were compared with the previous works that focused on waste classification. The difference between this work is the image dataset, which our work train and test the medical waste (facemask, glove, and syringe), while the previous works focused on general waste such as food, plastic, metal, paper, and others. From 2207 images of the medical waste, CNN and ResNet achieved 89.35 and 85.75% of accuracy, respectively, where it requires more images per class for the training improvement. YOLO v3 and YOLO v4 used 3073 images for training and achieved 84.86 and 89.21% of mean average precision (mAP). Our YOLO v3 mAP is in the average value among the previous works, while YOLO v4 has a higher mAP compared to the YOLO v4 training from other works. The YOLO v4 then was tested in real-time medical waste detection and successfully detected the masks, gloves, and syringe. However, there are still some wrong detections during the real-time detection using the camera, especially with other objects with similar shapes to the medical waste. Further, performance evaluations are required that can be used for medical waste objects and also for other different objects based on the applications. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. |
author2 |
57215719975 |
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57215719975 Moktar M.H. Hajjaj S. Mohamed H. Weng L.Y. |
format |
Conference paper |
author |
Moktar M.H. Hajjaj S. Mohamed H. Weng L.Y. |
author_sort |
Moktar M.H. |
title |
Performance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Works |
title_short |
Performance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Works |
title_full |
Performance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Works |
title_fullStr |
Performance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Works |
title_full_unstemmed |
Performance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Works |
title_sort |
performance review of modern ai algorithms utilized for medical waste sorting works |
publisher |
Springer Science and Business Media Deutschland GmbH |
publishDate |
2025 |
_version_ |
1825816084668743680 |
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13.244109 |