Machine learning techniques for mammography applications

Barnabò, Andrea (2017) Machine learning techniques for mammography applications. [Laurea magistrale], Università di Bologna, Corso di Studio in Fisica [LM-DM270], Documento full-text non disponibile
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During this work we will use machine learning and deep learning techniques in order to face up to some medical problems where they can play a basic role. In particular we will apply these algorithms to some mammography issues. The thesis presents three main experiments that are described below. The first one consists of a classification between nipples and non-nipples images. In this part of the work we will build a dataset composed by images belonging to these two classes. The main purpose here will be to build a classifier able to distinguish between nipple and non-nipple images. Several machine learning algorithms based on different models such as Support Vector Machine and Convolutional Neural Networks will be used in order to perform this task. In this experiment we will note the better classification capacity of the model based on Convolutional Neural Network. In the following section we will confront with an harder and usefull problem: the classification of tumoral masses vs non-tumoral masses. Therefore we will use a dataset composed by these two classes of images. We will perform again a classification either with Support Vector Machine or Convolutional Neural Networks. During this experiment we will obtain excellent results with the Convolutional Neural Networks and Support Vector Machine combined with a scattering network representation. The last part of the thesis consist of a realization of a complete CADx system . Here we will combine the models trained in the previous part and we will compare the results obtained by using them with the state of art.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Barnabò, Andrea
Relatore della tesi
Correlatore della tesi
Corso di studio
Curriculum E: Fisica applicata
Ordinamento Cds
Parole chiave
Machine Learning,Deep Learning,CADx,Mammography,CNN,Convolutional Neural Network,Scattering Network,SVM,Transfer learning,Data Augmentation,Breast Cancer,Tumoral Masses
Data di discussione della Tesi
15 Dicembre 2017

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