Intelligent System for Classification of Health-Care Waste Materials Using Convolutional Neural Network

One of the biggest problems faced by countries worldwide is the segregation of health-care waste. The generation of health-care waste is rising as the need for health care rises, eventually outpacing the demand. Currently, when health-care waste is collected from hospitals in India, it is not adequately separated and requires a significant amount of labor and time. To overcome these problems, the proposed work employed convolution neural network and deep learning model to identify and classify the health-care waste, to ease and automate the process that has been done manually. Here, the health-care waste data collected from NIT Trichy hospital and open-source data were combined to create a hybrid dataset of more than 850 images in each category. The deep learning technique was applied to 4000 images for identifying four distinct categories of health-care waste materials like gloves, masks, medicine, syringes and obtained an accuracy of 95.96%. Therefore, this proposed Health-care Waste Net (HWN) system deliver a very accurate deep learning-based solution for automatic identification and categorization of four major types of health-care waste.