Review on Deep Learning Based Biomedical Waste Detection and Classification

Abstract

Public health and the environment are in danger from the poor handling of biomedical waste produced by medical institutions and biomedical research institutes. The necessity for a system to detect and categorize biomedical waste products is brought on by the fact that the current human sorting procedure is not only ineffective but also risky for waste handlers and garbage collectors. In the existing system, the identified problem highlights the inefficiency and risks associated with manual sorting. In order to improve safety, effectiveness, and environmental sustainability in biomedical waste management practises, this study suggests a deep learning-based system that makes use of convolutional neural networks (CNNs) to reliably recognize and categorize items that are part of biomedical waste. The proposed approach might eventually achieve a 90% accuracy rate, which could result in cost savings and a decrease in the dangers related with manual sorting.