ABSTRACT
Globally, the pandemic of the coronavirus disease (COVID-19) is spreading quickly. Inadequate handling of contaminated garbage and waste management can unintentionally transmit the virus within the company. the complete spectrum from waste generation to treatment must be re-evaluated to scale back the socio-economic and environmental impacts of waste and help achieve a sustainable society. In the area of computer vision, deep learning is beginning to demonstrate high efficiency and minimal complexity. However, the problem now is the performance of the various CNN architectures with transfer learning compared to the classification of medical waste images. Using data augmentation, and preprocessing before performing the two-stage classification of medical waste classification. The research obtained an accuracy of 99.40%, a sensitivity of 98.18%, and a specificity of 100% without overfitting. © 2022 IEEE.