# Medical Waste Sorting: a computer vision approach for assisted primary sorting

arXiv: https://arxiv.org/abs/2303.04720 · HTML: https://mlsanity.com/paper/2303.04720/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 8 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Efficient Adaptive Ensembling for Image Classification](https://mlsanity.com/paper/2206.07394/) (2022)
- [Fine-Tuning Models Comparisons on Garbage Classification for Recyclability](https://mlsanity.com/paper/1908.04393/) (2019)

## Built on by (most cited first, 1 of 1)

- [Health Care Waste Classification Using Deep Learning Aligned with Nepal's Bin Color Guidelines](https://mlsanity.com/paper/2508.07450/) (2025)

## Abstract (arXiv)

Medical waste, i.e. waste produced during medical activities in hospitals, clinics and laboratories, represents hazardous waste whose management involves special care and high costs. However, this kind of waste contains a significant fraction of highly valued materials that can enter a circular economy process. To this end, in this paper, we propose a computer vision approach for assisting in the primary sorting of medical waste. The feasibility of our approach is demonstrated on a representative dataset we collected and made available to the community, with which we have trained a model that achieves 100% accuracy, and a new dataset on which the trained model exhibits good generalization.