Investigating the role of machine learning and deep learning techniques in medical image segmentation
Authors
Publication date
20 August 2021
Publisher
Università degli Studi dell'Insubria
Abstract
openThis work originates from the growing interest of the medical imaging community in the application of
machine learning techniques and, from deep learning to improve the accuracy of cancerscreening. The thesis
is structured into two different tasks.
In the first part, magnetic resonance images were analysed in order to support clinical experts in the
treatment of patients with brain tumour metastases (BM). The main topic related to this study was to
investigate whether BM segmentation may be approached successfully by two supervised ML classifiers
belonging to feature-based and deep learning approaches, respectively. SVM and V-Net Convolutional Neural
Network model are selected from the literature as representative of the two approaches.
The second task related to this thesisis illustrated the development of a deep learning study aimed to process
and classify lesions in mammograms with the use of slender neural networks. Mammography has a central
role in screening and diagnosis of breast lesions. Deep Convolutional Neural Networks have shown a great
potentiality to address the issue of early detection of breast cancer with an acceptable level of accuracy and
reproducibility. A traditional convolution network was compared with a novel one obtained making use of
much more efficient depth wise separable convolution layers.
As a final goal to integrate the system developed in clinical practice, for both fields studied, all the Medical
Imaging and Pattern Recognition algorithmic solutions have been integrated into a MATLAB® software
packageopenInformatica e matematica del calcologonella gloriaGonella, Glori