| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 1.08 MB | Adobe PDF |
Advisor(s)
Abstract(s)
Convolutional neural networks (CNNs) have recently been successfully used in the medical field to
detect and classify pathologies in different imaging modalities, including in mammography. One
disadvantage of CNNs is the need for large training datasets, which are particularly difficult to obtain
in the medical domain. One way to solve this problem is using a transfer learning approach, in which a
CNN, previously pre-trained with a large amount of labelled non-medical data, is subsequently finetuned
using a smaller dataset of medical data. In this paper, we use such a transfer learning approach,
which is applied to three different networks that were pre-trained using the Imagenet dataset. We
investigate how the performance of these pre-trained CNNs to classify lesions in mammograms is
affected by the use, or not, of normalised images during the fine-tuning stage. We also assess the
performance of a support vector machine fed with features extracted from the CNN and the combined
use of handcrafted features to complement the CNN-extracted features. The obtained results are
encouraging.
Description
Keywords
Mammographic image Convolutional neural Network Transfer learning Support vector machine Breast cancer Lesion classification