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Advisor(s)
Abstract(s)
Computer-Aided Detection/Diagnosis (CAD) tools were created to assist the detection
and diagnosis of early stage cancers, decreasing false negative rate and improving radiologists’
efficiency. Convolutional Neural Networks (CNNs) are one example of deep learning
algorithms that proved to be successful in image classification. In this paper we aim to study
the application of CNNs 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, wich
is based on three different networks that were pre-trained on the Imagenet dataset. We then
investigate the performance of these pre-trained CNNs 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.