A hyperspectral image (HSI) is a 3D hypercube that incorporates spatial and spectral data. It’s an emerging optical imaging method especially in the field of medical imaging, allowing detailed introspection of the spectral data from biological tissues. However, this imaging technique is susceptible to various types of noise, such as thermal noise, speckle noise, Poisson noise, and Gaussian noise. This research article investigates the denoising of hyperspectral images in a multidimensional choledochal liver bile duct cancer dataset that is publicly available in kaggle. Initially, we synthetically add different types of noises to the original data. The noise is then removed using various filters, and the denoising performance is evaluated based on several metrics. The results of this study provide information regarding the efficiency of multiple filters in reducing noise in HSI.