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Medical imaging is an essential element in healthcare, especially for the early diagnosis and treatment of illnesses, including breast cancer. Breast cancer is an illness with a high mortality rate that mostly affects women worldwide. Currently, histopathology image analysis is one of the rising research areas in cancer diagnosis. With digital images, the demand for more accurate and visually pleasing images increases. However, the nature of images is normally degraded by noise which may affect image quality. Nowadays, image denoising has become a common practice in image processing tasks to preserve the quality of images for imaging analysis. Therefore, this study compares the performance of several image denoising methods for histopathology images. The BreakHis dataset is used in this study, which consists of 7909 histopathology images from 82 patients in Parana, Brazil. This study compares several denoising methods such as Gaussian smoothing, anisotropic diffusion, wavelet transform and shearlet transform, when standardized noise was presented on the histopathology images. The performance of denoising methods was examined based on the Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE). The results showed that the shearlet provides more efficient performance in denoising the histopathology images than other methods. Shearlets provide an optimally sparse representation of 2D images with multiresolution and multiscale properties. It can be concluded that the shearlet is the best image denoising method for breast cancer histopathology images. The research findings are expected to facilitate the researchers or radiologists in image processing tasks to obtain high-quality images prior to classifying images into benign or malignant tumours. [ABSTRACT FROM AUTHOR]