Smart Healthcare: Pill Identification with Deep Learning and Voice Assistance

Abstract

Medical pill identification is crucial for ensuring medication safety, particularly for visually impaired individuals or those managing multiple prescriptions. This project leverages deep learning techniques using Artificial Neural Networks (ANN) to identify pills through images captured via a camera or uploaded by the user. The process involves a user selection dialog, image upload processing, and camera-based image processing to facilitate drug identification. The system automates pill identification by pre-processing images to enhance quality, extracting meaningful features, and employing an ANN for classification. Results are relayed, providing comprehensive drug information retrieved from the identified pill, accessible through a voice output system for improved accessibility. Existing systems primarily use traditional image processing techniques or manual identification through databases, which are time-consuming and less accurate in diverse real-world conditions. A significant drawback of these systems is their inability to handle poor lighting, varied angles, or partial occlusions in captured images effectively. This work utilizes cutting-edge deep learning algorithms, focusing on the healthcare domain, to improve the accuracy and usability of pill identification.