A Survey Paper on Medical Waste Classification

Proper classification and disposal of medical waste are imperative for safeguarding public health and environmental well-being. Accurate sorting of medical waste is crucial in mitigating the risk of infection and disease transmission. Traditional methods rely on color-coded containers and manual sorting, which can be time-consuming and labor-intensive. The suggested techniques use deep neural networks to automate the classification process. These neural networks have been taught to quickly and effectively recognize and categorize several forms of medical waste. This automation considerably enhances the waste management process's efficiency, streamlining the entire process. Furthermore, connectivity with IoT devices enables real-time monitoring and data collecting. This technology convergence enables healthcare institutions to make more informed decisions and optimize their waste management procedures. Combining automatic classification and IoT-driven monitoring improves productivity while lowering the margin for physical contact, ultimately improving public health and environmental safety. This survey explores different approaches that revolutionize medical waste management, making it a crucial step toward a safer and more efficient healthcare ecosystem.