UMobile-Net: A Dual-Model Deep Learning Framework for Pill Image Detection

Pill image detection is essential in healthcare applications such as hospital workflow optimization, assistance for visually impaired individuals, and elderly care. However, high inter-class similarity and varying imaging conditions make accurate recognition challenging. This paper presents UMobile-Net, a dual-model deep learning framework integrating a segmentation model (U-Net or W 2) with a MobileNet-based classifier in a two-stage pipeline. In stage one, segmentation masks derived from dataset annotations are refined to extract pill regions, followed by image enhancement. In stage two, the processed images are classified using a customized MobileNet architecture. Evaluations on the CURE and OGYEI-v2 datasets show that UMobile-Net consistently surpasses existing methods. The U-Net–based variant achieved accuracies of 97.78% (CURE) and 97.62% (OGYEI-v2), while the W 2 –based variant achieved 95.85% and 97.61%, respectively. The results confirm the robustness and accuracy of the proposed approach for real-world pill recognition.