Digital mammograms are an important imaging modality for breast cancer screening and diagnosis. Several types of noise appear in mammograms and make the job of detecting breast cancer even more challenging due to missing details in the information of the image.

In this study, we analyze the effect of mammogram images quality on the performance of the Deep Convolutional Neural Network on a mammogram images classification task. Thus, our objective is to show how the classification accuracy varies with the application of a denoising step.

Indeed, we investigated two different approaches to breast cancer detection. The first is the classification of the original mammo-gram images without being denoised, and the second is the classification of mammogram images that are denoised using a Deep Convolutional Neural Network, Wiener filter and Median filter. Therefore, the mammogram images are first denoised using each of the three denoising methods and then, classified into two classes: cancer and normal, using AlexNet, a pre-trained Deep Convo-lutional Neural Network in order to show whether the denoising method used is effective when grafted onto a Deep Convolutional Neural Network by measuring accuracy, sensitivity, and specificity.

Interesting results are achieved where the DCNN denoising step improved the Deep Convolutional Neural Network classification task with an increase of 3.47% for overall accuracy, 5.34% for overall specificity, and 0.56% for overall sensitivity.