Analyzing Glucose Levels through Image Processing and Deep Learning Methods (International Journal of Scientific Research in Science and Technology, 2024)
This study investigates a non-invasive technique for analyzing glucose content by utilizing image processing and Convolutional Neural Networks (CNNs). Conventional glucose monitoring methods typically require invasive procedures, which can pose risks to the immune system, especially with frequent injections. To address these risks, the research focuses on a non-intrusive approach that uses urine samples for glucose analysis. The method includes image processing tasks such as applying a Gaussian filter and resizing images to prepare the input for a CNN. Deep learning, specifically with CNNs, is applied due to its effectiveness in feature extraction and pattern recognition. The aim is to achieve high accuracy in classifying glucose levels into categories like diabetes, pre-diabetes, and normal, providing a reliable and non-invasive alternative to standard glucose monitoring methods. This innovative technique could lead to improved patient compliance and better overall health monitoring for those who need regular glucose assessments.