Class Imbalance Pill Image Classification via Ensemble Learning

Accurate identification of pill shapes is crucial for medication management and safety, acting as a safeguard against medication errors, especially with the increasing variety of pills that pose risks to patients. To address this, we propose a novel model for class imbalance pill image classification with the use of transfer learning and ensemble learning. Our proposed ensemble model integrates custom built Convolutional Neural Networks (CNN) and pre-trained EfficientNet-B2, both of which excel in image classification tasks. The CNN architecture employs multiple convolutional and pooling layers to identify pill characteristics, while EfficientNet enhances performance through its pre-trained capabilities. The proposed ensemble model demonstrated exceptional performance, achieving accuracies of 99.40% on the NIH Pill Image Dataset and 100% on the PillBR Dataset, respectively.