Pharmaceutical Image Recognition Using Deep Learning

Pharmaceutical Image Recognition Using Deep Learning Project Description: This project focuses on the automatic identification of pharmaceutical pills using deep learning and computer vision techniques. It addresses the critical issue of medication errors due to misidentification, particularly when pill labels are damaged or missing. The system is built using TensorFlow and Keras, employing Convolutional Neural Networks (CNNs) to detect and classify pills based on their physical features such as color, shape, and size. A large dataset of pill images—including various types with different angles—is used for training and testing. The project utilizes image pre-processing techniques like normalization, resizing, and data augmentation. Advanced object detection algorithms such as OpenCV’s GrabCut are applied for precise segmentation. Once a pill image is input via a camera, the model performs real-time identification by matching the image with a pre-trained dataset and retrieves relevant information such as the pill name, composition, dosage, and use cases. The system aims to support pharmacists, visually impaired users, and general patients in safe medicine identification. This research also explores the integration of this model into mobile or web applications for broader accessibility and includes performance evaluation with accuracy metrics. Technologies Used: Python TensorFlow Keras OpenCV CNN (Convolutional Neural Network) Image Preprocessing & Augmentation Pillbox / PIRD Dataset Key Features: Real-time pill recognition via camera input Accurate detection based on shape, color, and size Dataset-driven training with high accuracy (~91%) Dashboard displaying drug details and use cases Potential mobile app integration for broader utility