Publications by authors named "Penchun Saenprasarn"
This study utilizes spectral analysis to quantify water pollutants by analyzing the images of biological oxygen demand (BOD). In this study, a total of 2545 images depicting water quality pollution were generated due to the absence of a standardized water pollution detection method. A novel snap-shot hyperspectral imaging (HSI) conversion algorithm has been developed to conduct spectral analysis on traditional RGB images.
View Article and Find Full Text PDFArticle Synopsis
- Traditional glaucoma diagnostics use standard fundus images to assess features like the optic cup-to-disc ratio but may miss important details.
- This study introduces a new hyperspectral imaging technique that detects changes in oxygen saturation within retinal vessels, providing a richer analysis for diagnosing glaucoma.
- By employing machine learning algorithms like the Vision Transformer, researchers found that a specific spectral band (610-780 nm) achieved high accuracy in classification, suggesting that hyperspectral imaging could revolutionize how glaucoma is diagnosed compared to conventional methods.
This study pioneers the application of artificial intelligence (AI) and hyperspectral imaging (HSI) in the diagnosis of skin cancer lesions, particularly focusing on Mycosis fungoides (MF) and its differentiation from psoriasis (PsO) and atopic dermatitis (AD). By utilizing a comprehensive dataset of 1659 skin images, including cases of MF, PsO, AD, and normal skin, a novel multi-frame AI algorithm was used for computer-aided diagnosis. The automatic segmentation and classification of skin lesions were further explored using advanced techniques, such as U-Net Attention models and XGBoost algorithms, transforming images from the color space to the spectral domain.
View Article and Find Full Text PDF