2025

Journal

Imapct Factor 2024: 3.6

Mammogram images are subject to various types of noise, which restricts the analysis of images and diagnosis. Mammogram image denoising is very important to improve image quality and to make the segmentation and classification results more correct. In this work, we propose a Deep Convolutional Neural Network (DCNN) to denoise the mammogram images in order to improve the image quality by handling Gaussian, Speckle, Poisson, and Salt and Pepper noise. The main objective of this study is to remove different types of noises from mammogram images and to maximize the quantity of information content in the enhanced images. We first add noise models to mammogram images and then enhance the image by removing the noise using DCNN. Furthermore, we compare our results with state-of-the-art denoising methods, such as the Adaptive Median filter, Wiener filter, Gaussian filter, Median filter, and Mean. Three datasets have been used, including Digital Database for Screening Mammography (DDSM), mini-Mammographic Image Analysis Society (mini-MIAS), and a local Tunisian dataset. The experimental results show that DCNN has a better denoising performance than the other methods, with an average PSNR range of 46.0-51.83 dB and an average SSIM range of 0.988-99.83, which may suggest its adaptability to different models of noise.

Mechria, H., Hassine, K. & Gouider, M.S. Mammogram images denoising based on deep convolutional neural network. Multimed Tools Appl (2025). https://doi.org/10.1007/s11042-024-20569-1