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Hospitals create an enormous volume of potentially hazardous trash. Currently, bandages, plastic papers, wasted pus, syringes, glucose drip bottles, other dangerous medical wastes, and containers are separated by hand, resulting in long-term health consequences such as tuberculosis, cancer, and infectious diseases. Rag pickers are currently responsible for the majority of waste separation. Efficient management of medical waste demands not only precise segregation but also real-time monitoring and notification systems to ensure timely intervention and optimized waste handling. This project proposes an integrated solution combining deep learning, image processing, Embedded Systems hardware and Internet of Things (IoT) technologies. Medical waste will be detected using the Deep Learning technique; A deep learning model is trained on a diverse dataset to classify medical waste items, and image processing techniques are utilized to enhance the accuracy of the classification process. This holistic approach offers an efficient and automated system for medical waste management integrating waste segregation, waste level monitoring, and timely notifications, thereby ensuring a sustainable and responsible waste management process in healthcare facilities.
- et al. (Thu,) studied this question.