Brain Tumor Growth Modelling .

Kamel, Adel (2021) Brain Tumor Growth Modelling . Doctoral thesis, Université de mohamed kheider biskra.

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Prediction methods of Glioblastoma tumors growth constitute a hard task due to the lack of medical data, which is mostly related to the patients’ privacy, the cost of collecting a large medical dataset, and the availability of related notations by experts. In this thesis, we study and propose a Synthetic Medical Image Generator (SMIG) with the purpose of generating synthetic data based on Generative Adversarial Network in order to provide anonymized data. In addition, to predict the Glioblastoma multiform (GBM) tumor growth we developed a Tumor Growth Predictor (TGP) based on End to End Convolution Neural Network architecture that allows training on a public dataset from The Cancer Imaging Archive (TCIA), combined with the generated synthetic data. We also highlighted the impact of implicating a synthetic data generated using SMIG as a data augmentation tool. Despite small data size provided by TCIA dataset, the obtained results demonstrate valuable tumor growth prediction accuracy

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Tumor growth prediction, Generative Adversarial Network, Glioblastoma multiform, convolution neural network
Subjects: Q Science > Q Science (General)
Divisions: Faculté des Sciences Exactes et des Sciences de la Nature et de la Vie > Département d'informatique
Depositing User: BFSE
Date Deposited: 12 Apr 2022 07:46
Last Modified: 12 Apr 2022 07:46

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