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
On the path of life since we were born, we have encountered many things in life, differences and various characteristics. However, each factor of each person's life has different responsibilities, dreams, and life context differences. Everyone still has to struggle against obstacles and many burdens in life, shouldering the responsibilities of themselves and their families in order to survive. Living in different ways, with many burdens and dreams, but in real life, how many people can shoulder these burdens to reach their dreams?
คณะบริหารธุรกิจ
CO Breathalyzer with Voice Response is the device to measured the level of CO residual in a person's lung who consume tobacco. Measuring residual CO in human breath can identify the tobacco addiction level instead of measuring nicotine in blood.
คณะวิทยาศาสตร์
Microalgae are rich in bioactive compounds that may contribute to the growth of probiotics, which require appropriate nutrients, known as prebiotics, to thrive. This study aims to evaluate the effectiveness of crude extracts from intracellular components residues of the microalga Chlorella sp. KLSc61 in promoting the growth of the probiotic bacterium Lactiplantibacillus plantarum JCM1149 under simulated gastrointestinal conditions. The intracellular extracts were obtained using 70% (v/v) ethanol, and their effects on probiotic growth were tested at concentrations of 0.1%, 0.75% and 1.5%. The growth of Lactiplantibacillus plantarum JCM1149 was assessed using the drop plate method. The findings of this study will provide insights into the potential of Chlorella sp. KLSc61 extracts in enhancing probiotic growth, which could lead to the development of synbiotic dietary supplements containing both probiotics and prebiotics. Additionally, this study may serve as a foundation for further research on the role of microalgal extracts in gut health and immune system modulation.