Medical Waste Classification using Deep Learning and Convolutional Neural Networks

Abstract

With the rise of attention to healthcare since the start of the century, which the recent pandemic has emphasized, the number of hospitals and clinics has increased exponentially. The growth in hospitals and patients has also resulted in increased medical waste. The different kinds of medical waste must be segregated and disposed of properly to prevent the spread of bacteria and viruses and cross-contamination. However, it is not economically feasible to hire a workforce that can segregate said waste. With the perceived popularity of deep learning and image classification systems, creating a Deep Learning model to categorize the different kinds of medical waste is possible. Hence using a deep learning-based classification method in which an appropriate pre-trained model is selected for practical implementation, followed by transfer learning methods to improve classification results, is appropriate. Different types of medical waste are grouped into umbrella categories(general, hazardous, infectious). An ideal situation would be where images are uploaded, and the machine can classify the presented waste appropriately with little to no waiting times. Three out of four of the modified pre-trained models with different architectures were able to achieve an accuracy above 95 percent.