Smart MedWaste: medical waste management with IOT and deep learning

Abstract

Pharmaceutical-medical waste management stands as a critical process within hospital organizations, its mishandling potentially leading to hazardous repercussions such as mass infections. Inadequate waste management practices pose a significant threat to environmental health and safety. Addressing this challenge requires the implementation of advanced solutions capable of scientifically managing medical waste within the workplace. This paper introduces an Automated Waste Management System (AWMS) empowered by IoT and deep learning technologies, designed to streamline the classification, treatment, and disposal of biomedical waste. By leveraging cutting-edge technology, our system aims to significantly reduce the time and energy expenditure associated with waste management services while concurrently minimizing waste generation. The proposed system operates through a userfriendly interface, wherein medical waste is deposited into a Garbage Collector Pan (GCP). A ResNeXt deep learning model, integrated into the system, swiftly classifies the waste with an impressive accuracy of 97.93% and directs it to the specialized Trash Collector Bins (TCB) based on its classification. Through this innovative approach, our system not only organizes but also enhances the accuracy of medical waste management, ensuring optimal utilization of resources and preserving environmental integrity. Additionally, the AWMS monitors the fill level of TCB and notifies hospital authorities when bins require emptying, ensuring timely waste disposal and efficient utilization of resources.