AI-Enabled Approach for Classification of Medical Waste in Healthcare Facilities
Keywords:
Artificial Intelligence, Automation, Computer Vision, Deep Learning, Medical Waste Management, Smart BinAbstract
Medical waste needs to be handled with care, and their project, the AI-Based Smart Trash Bin, makes this process safer and smarter. Using Artificial Intelligence, the bin can automatically detect whether the waste is infectious, a sharp item, or general hospital waste, and then open the correct lid-all without any touch. This work presents the design and development of an AI trash bin capable of recognizing and categorizing medical waste using computer vision, deep learning algorithms, and automated actuation mechanisms. The system uses a camera to capture waste images, processes them with a trained Convolutional Neural Network (CNN), and directs the waste to the appropriate compartment using servo motors controlled by a microcontroller. The prototype was tested on a medical waste dataset and demonstrated classification accuracy above 90%, with response time under 2 seconds. They use an ESP32 or Arduino, a camera, and servo motors to control the lids. The Al model, trained using Teachable Machine or TensorFlow Lite, runs directly on the device, so no internet is required. The system also gives quick feedback using LEDs or voice prompts. This bin improves hygiene, ensures proper waste separation, and can be easily used in hospitals to make waste management smarter and safer. By minimizing human involvement in biomedical waste handling, the system not only reduces occupational hazards but also enhances segregation efficiency and supports sustainable healthcare waste management. This solution can be scaled for hospitals, diagnostic labs, and clinics to meet growing global healthcare challenges.
References
P. Akkajit and A. Sukkuea, “Medical Waste Classification Using Convolutional Neural Network,” E3S Web of Conferences, vol. 530, p. 04001, 2024,
H. Zhou, X. Yu, A. Alhaskawi. “A deep learning approach for medical waste classification,” Scientific Reports, vol. 12, no. 1, Feb. 2022,
X. Xu, C. Wang, “MedBin: A lightweight End-to-End model-based method for medical waste management,” Waste Management, vol. 200, pp. 114742–114742, Mar. 2025
A. Alourani, M. U. Ashraf, and M. Aloraini, “Smart waste management and classification system using advanced IoT and AI technologies,” PeerJ Computer Science, vol. 11, pp. e2777–e2777, Apr. 2025,
A. U. Gondal, M. I. Sadiq, “Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron,” Sensors, vol. 21, no. 14, p. 4916, Jul. 2021
A. A. Mustapha, S. ‘Atifah Saruchi, H. Supriyono, and M. I. Solihin, “A Hybrid Deep Learning Model for Waste Detection and Classification Utilizing YOLOv8 and CNN,” The 8th Mechanical Engineering, Science and Technology International Conference, p. 82, Mar. 2025
S. Poudel and P. Poudyal, “Classification of Waste Materials using CNN Based on Transfer Learning,” Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation, Dec. 2022,
H. Ahmed Khan, S. S. Naqvi, A. A. K. Alharbi, S. Alotaibi, and M. Alkhathami, “Enhancing trash classification in smart cities using federated deep learning,” Scientific Reports, vol. 14, no. 1, p. 11816, May 2024
X. Cai, F. Shuang, X. Sun, Y. Duan, and G. Cheng, “Towards Lightweight Neural Networks for Garbage Object Detection,” Sensors, vol. 22, no. 19, p. 7455, Sep. 2022
M. M. Islam, M. Mahedy, M. R. Hossain, M. P. Uddin, and M. A. Mamun, “Towards sustainable solutions: Effective waste classification framework via enhanced deep convolutional neural networks,” PLoS ONE, vol. 20, no. 6, pp. e0324294–e0324294, Jun. 2025
G. White, C. Cabrera, A. Palade, F. Li, and S. Clarke, “WasteNet: Waste Classification at the Edge for Smart Bins,” arXiv:2006.05873 [cs], Jun. 2020,
Z. Qiao, “Advancing Recycling Efficiency: A Comparative Analysis of Deep Learning Models in Waste Classification,” arXiv.org, 2024.
M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification,” Neurocomputing, vol. 321, pp. 321–331, Dec. 2018
P. Jaikumar, R. Vandaele, and V. Ojha, “Transfer Learning for Instance Segmentation of Waste Bottles using Mask R-CNN Algorithm,” Arxiv:2204.07437, Apr. 2022
E. Ko and P. Hao, “Object Detection and Classification for Waste Disposal.”
R. Uppala and R. V., “Develop a 7 Layers Convolution Neural Network and IoT-Based Garbage Classification System,” International Journal of Intelligent Systems and Applications in Engineering, 2026.
M. H. B. Moktar, S. S. H. Hajjaj, and H. Mohamed, “Medical Waste Detection and Classification Through YOLO Algorithms,” Lecture Notes in Networks and Systems, pp. 22–33, 2024
M. H. Moktar, H. Mohamed, Sami, and M. Z. Baharuddin, “Medical waste sorting machine development with IoT and YOLO model utilization,” Journal of Engineering and Applied Science, vol. 72, no. 1, Jun. 2025
Van and P. Roshan, “Improving Medical Waste Classification with Hybrid Capsule Networks,” Arxiv.org, 2025.
N.-B.-Q. Nguyen, T.-M. Do, C.-T. Phan, and T.-T.-H. Phan, “Towards Accurate and Efficient Waste Image Classification: A Hybrid Deep Learning and Machine Learning Approach,” arXiv.org, 2025.
K. N. Sami, Z. M. A. Amin, and R. Hassan, “Waste Management Using Machine Learning and Deep Learning Algorithms,” International Journal on Perceptive and Cognitive Computing, vol. 6, no. 2, pp. 97–106, Dec. 2020.
S. Kruthika M, R. R, and S. D, “Garbage Classification: A Deep Learning Perspective,” Deleted Journal, vol. 2, no. 12, pp. 2774–2780, Dec. 2024.
D. Ghosh and A. Goswami, “HybridSOMSpikeNet: A Deep Model with Differentiable Soft Self-Organizing Maps and Spiking Dynamics for Waste Classification,” arXiv.org, 2025.
M. M. Hossen, “GCDN-Net: Garbage classifier deep neural network for recyclable urban waste management,” Waste Management, vol. 174, pp. 439–450, Feb. 2024
D. Shafek, H. W. Hilow, and M. Ahmed, “Classification of waste images using deep learning technique,” Journal of Applied Research and Technology, vol. 23, no. 4, pp. 381–391, Aug. 2025
I. Dawar, A. Srivastava, M. Singal, N. Dhyani, and S. Rastogi, “A systematic literature review on municipal solid waste management using machine learning and deep learning,” Artificial Intelligence Review, vol. 58, no. 6, Mar. 2025
