Triple Sensors Integrated Deep Learning based Network for Detection of Defects in Medicines

Accurate medicine dispensing is extremely important in all hospitals, as medical errors can cause serious health issues, underscoring the need for improved protocols. Numerous research has been conducted on developing detection techniques for unpackaged pills or drugs with similar packages. However, existing frameworks face issues like dispensing errors and accurate defect prediction. To overcome these challenges, a novel Tri-SID Medi technique has been proposed for detecting the defects in medicines and delivering defect-free medicines using tri sensors such as ultrasonic sensor, Weight Sensor, and GPS. Initially, the collected drug images from the hospital/pharmacy are preprocessed using the adaptive unsharp mask-guided filter to enhance the image quality and reduce noise artifacts. Then the pre-processed images are fed into APM-YOLO to detect the images as defect or non-defect. The non-defect medicines are given into the first Similarity Group (SG), in which the Squeeze and Excitation-based Bidirectional Long Short-Term Memory (SE -BiLSTM) framework is trained for grouping the medicinal products. Finally, the defect-free group medicines are directly delivery to the customers in the delivery phase by using tri sensors and Global System for Mobile Communications. The proposed Tri-SID Medi method achieves 99.03% accuracy which is 9.11%, 2.45%, and 5.07% better than the existing techniques such as YOLO, DCNN, and BERT. The experimental results of the proposed techniques highlight the potential for real-time drug defect detection and similar drug groupings with diverse packaging, which can reduce medical errors and are key to ensuring patient safety.