Highlighted Deep Learning based Identification of Pharmaceutical Blister Packages

Precise identification of blister packages carries utmost importance at dispensing stations, where numerous prescriptions are to be efficiently dispensed by pharmacists. However, the usual presence of several hundreds of similarly looking, but completely different types of blister packages, in a crowded dispensing station makes it prone to human error, posing serious safety and health concerns for a patients life. In this work, we propose a highlighted deep learning (HDL) based approach for accurate identification of blister packages. HDL allows smart manipulation on raw data in order to better segment the identified targets and to expose inherent descriptive features, thus facilitating an accurate deep learning based classification process. Specifically, HDL uses automatic detection and then segments and processes the raw blister pack images irrespective of position, lighting variations, etc., making them suitable for CNN to classify the correct blister pack types. A ResNet CNN classifier has been trained using the processed images, and the resultant CNN model is finally deployed for classification. We have conducted an extensive experiment at the adult lozenge dispensing station of MacKay Memorial Hospital. The database that will be released along with this paper, consists of 272 types of blister packages, with 65 images belonging to each type for training and additional 7 images for validation. On real testing scenario, the proposed approach yielded almost 100% accuracy, consistently over the entire testing set. The proposed solution can also be extended to any dispensing station for automatic detection and classification of blister packages, thereby offering an highly effective and efficient delivery system.