AUTOMATED PILL RECOGNITION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS

Abstract

Accurate detection and identification of pharmaceutical pills has become an essential component of modern healthcare automation, ensuring medication safety, reducing dispensing errors, and supporting efficient clinical workflows. With the rapid growth of digital health systems and the global need for reliable medication verification tools, deep learning—particularly Convolutional Neural Networks (CNNs)—has emerged as a powerful approach for pill recognition. Existing research demonstrates considerable advancements in pill detection, fine-grained image classification, metric-learning–based retrieval, and real-time recognition across mobile and clinical platforms through methods such as deep convolutional feature extraction, proxy-based learning, lightweight mobile architectures, and domainadaptive training frameworks [1–14]. Recent studies emphasize the importance of robust datasets, multicenter clinical evaluations, and optimized CNN pipelines capable of handling variations in pill color, shape, imprint, lighting, and occlusion, supported by curated repositories, mobile capture environments, and real-world deployment scenarios [4–7, 15–20]. Building upon these developments, this work proposes a CNN-driven pill detection framework that integrates fine-grained visual feature learning, imprint-focused representation modeling, and high-precision classification to accurately differentiate visually similar pills. Leveraging insights from metric learning, mobile-optimized CNN architectures, and explainable deep learning strategies, the system aims to enhance identification accuracy while minimizing misclassification in challenging imaging conditions. This study contributes a unified analysis of traditional image-processing techniques and contemporary deep-learning approaches, highlighting their strengths, limitations, and applicability in clinical, industrial, and patient-centric environments. The proposed model aligns with emerging trends in intelligent medication verification and offers a scalable, reliable, and interpretable solution for real-world healthcare ecosystems.