This study explores the application of deep convolutional neural networks (CNNs) for accurate pill identification, addressing the limitations of traditional human-based methods. Using a dataset of 1,250 images across 10 household remedy drugs, various CNN architectures, including YOLO models, were tested under different conditions. Results showed that natural lighting was optimal for imprinted pills, while a lightbox improved detection for plain pills. The YOLOv5-tiny model demonstrated the best detection accuracy, and efficientNet_b0 achieved the highest classification performance. While the model showed strong results, its generalization is limited by sample size and drug variability. Nonetheless, this approach holds promise for enhancing medication safety and reducing errors in outpatient care.
The increasing complexity of pharmaceutical treatments requires precise pill identification to ensure patient safety. Traditional methods for pill reconciliation rely on human experts, which are time-consuming and prone to errors. Deep Convolutional Neural Networks (CNNs), particularly effective in image processing, offer a promising solution for automating and enhancing these processes.
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
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คณะอุตสาหกรรมอาหาร
In the development of high protein jasmine rice products, hydrocolloids, HPMC at 0, 0.25, 0.5 and 1% w/v and MD at 10% w/v were used. This hydrocolloid contained 30% w/v dissolved protein and was coated with raw jasmine rice. It was found that different amounts of HPMC affected the adhesion of proteins in rice. Then, the hydrocolloid with the best adhesion, 0.25% w/v, was used to find the optimum amount for coating rice at ratios of 1:3 and 1:5, which affected protein content, texture, color, water retention and sensory acceptability.
วิทยาลัยวิศวกรรมสังคีต
This project studies how to design a portable, sound-confining space that allows users to practice using their voices without disturbing the surroundings.