Advancing biomedical waste classification through a hybrid ensemble of deep Learning, reinforcement Learning, and...

The complex nature of pharmaceutical and biomedical waste poses significant challenges for effective management, particularly in the safe and cost-intensive disposal of infectious materials. This research presents a novel classification model that utilizes a double heterogeneous ensemble integrating deep learning, reinforcement learning, and differential evolution algorithms for waste classification. The model operates through three key phases: image augmentation, ensemble image segmentation, and ensemble convolutional neural network architectures, employing decision fusion techniques that incorporate reinforcement learning and differential evolution. It integrates various image segmentation methods, including U-Net, Mask R-CNN, DeepLab Version 3 Plus, and convolutional neural network architectures such as Inception Version 3, Residual Network 50, Mobile Network Version 2, and Densely Connected Convolutional Network 121.The developed model powers the "Biosorter," a machine specifically designed to differentiate between infectious and non-infectious waste. Comprehensive evaluations conducted on both proprietary and benchmark datasets demonstrate that the proposed BioSorter model significantly outperforms several widely used deep learning architectures-including ResNet50, DenseNet121, MobileNetV2, InceptionV3, and ResNeXt-50. On average, the model achieved classification improvements of 5.35% and 9.05% in accuracy over these methods on the respective datasets. During real-world deployment at a small medical center, the BioSorter achieved 98% sorting accuracy and a 50% increase in processing throughput. Furthermore, a post-deployment usability assessment was conducted using the System Usability Scale (SUS)-a standardized questionnaire commonly used to evaluate perceived ease of use of interactive systems. The BioSorter received a score of 93.5 on the SUS (out of 100), reflecting a high level of user-perceived efficiency and interface simplicity during operational use. This study represents a significant advancement in waste management technology, offering potential to reduce disposal costs and enhance sustainability in healthcare environments.