| Resumo: | Computer-Aided Detection/Diagnosis (CAD) tools were created to assist the detection and diagnosis of early-stage cancers, decreasing the false negative rate and improving radiologists’ efficiency. Convolutional Neural Networks (CNNs) is one example of deep learning algorithms that proved to be successful in image classification. In this paper, we aim to study the application of CNN's to the classification of lesions in mammograms. One major problem in the training of CNNs for medical applications is the large dataset of images that is often required but seldom available. To solve this problem, we use a transfer learning approach, which is based on three different networks that were pre-trained on the Imagenet dataset. We then investigate the performance of these pre-trained CNN's and two types of image normalization to classify lesions in mammograms. The best results were obtained using the Caffe reference model for the CNN with no image normalization.
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