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Proceedings Paper

Classification of brain tumors using personalized deep belief networks on MRImages: PDBN-MRI
Author(s): Ahmed Kharrat; Mahmoud Néji
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Paper Abstract

As of late, various studies used deep learning methods on brain tumor data. There are two principle tasks used for brain tumor classifications: feature extraction and brain tumor classification. The motive of this study is to analyze various images of the BraTS 2015 dataset by using deep learning algorithms and to classify the brain tumors to different types of regions: whole, core and enhanced. We address the issue by using deep belief networks, a kind of neural network that detects features in images and classifies them. This paper introduces a three-step framework for classifying multiclass radiography images. The first step utilizes a de-noising technique based on vanilla data preprocessing to remove noise and insignificant features of the images. For learning the unlabelled features we used unsupervised deep belief network (DBN) in the second step. In the small-scale DBNs have demonstrated significant potential but when scaling to large networks the computational cost of training the restricted Boltzmann machine is a major issue. Discriminative feature subsets obtained in the first two steps serve as inputs into classifiers in the third step for evaluations. Our goal is a machine capable of recognizing a brain tumor’s type; we define a probabilistic model that classifies brain tumors to different types of regions having as input the best preprocessing vanilla (Image size 256×256). Using the BraTS data to train the deep belief networks proves an accuracy of 91.6% on its classifications.

Paper Details

Date Published: 15 March 2019
PDF: 9 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412M (15 March 2019); doi: 10.1117/12.2522848
Show Author Affiliations
Ahmed Kharrat, Univ. of Sfax (Tunisia)
Mahmoud Néji, Univ. of Sfax (Tunisia)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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