The Influence of Image Normalization in Mammographic Classification with CNNs - Perre, Ana Catarina (2020)
In order to improve the performance of Convolutional Neural Networks (CNN) in the classification of mammographic images, many researchers choose to apply a normalization method during the pre-processing stage. In this work, we aim to assess the impact of six different normalization methods in the classification performance of two CNNs. Results allow us to concluded that the effect of image normalization in the performance of the CNNs depends of which network is chosen to make the lesion classification; besides, the normalization method that seems to have the most positive impact is the one that subtracts the image mean and divide it by the corresponding standard deviation (best AUC mean with CNN-F = 0.786 and with Caffe = 0.790; best run AUC result was 0.793 with CNN-F and 0.791 with Caffe).