A Study of Medical Waste Classification using Deep Learning

Efficient medical waste management is critical for protecting public health and the environment. This study explores the application of deep learning to automate medical waste sorting, addressing a growing need for scalable waste management solutions. By utilizing TensorFlow and PyTorch frameworks, the proposed approach accurately classifies medical waste samples, for sorting and safe disposal. Key aspects such as data preprocessing, model architecture, and training methodologies are thoroughly examined, and achieved classification accuracy of approximately <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 8. 5 2 \%}$</tex>. These findings highlight the potential of deep learning in enhancing healthcare waste management systems and paving the way for more sustainable waste practices.