MRI brain scan classification using novel 3-D statistical features

Conference paper

Hasan, A, Meziane, F., Aspin, R and Jalab, HA 2017. MRI brain scan classification using novel 3-D statistical features. Second International Conference on Internet of Things, Data and Cloud Computing. Cambridge, UK 22 - 23 Mar 2017 ACM Press.
AuthorsHasan, A, Meziane, F., Aspin, R and Jalab, HA
TypeConference paper
Abstract

The paper presents an automated algorithm for detecting and classifying magnetic resonance brain slices into normal and abnormal based on a novel three-dimensional modified grey level co-occurrence matrix approach that is used for extracting texture features from MRI brain scans. This approach is used to analyze and measure asymmetry between the two brain hemispheres, based on the prior-knowledge that the two hemispheres of a healthy brain have approximately a bilateral symmetry. The experimental results demonstrate the efficacy of our proposed algorithm in detecting brain abnormalities with high accuracy and low computational time. The dataset used in the experiment comprises 165 patients with 88 having different brain abnormalities whilst the remaining do not exhibit any detectable pathology. The algorithm was tested using a ten-fold cross-validation technique with 10 repetitions to avoid the result depending on the sample order. The maximum accuracy achieved for the brain tumors detection was 93.3% using a Multi-Layer Perceptron Neural Network..

KeywordsLinear discriminant analysis; ; Magnetic resonance imaging; ; Modified grey level co-occurrence matrix; ; Multi-layer perceptron neural network support vector machine
Year2017
ConferenceSecond International Conference on Internet of Things, Data and Cloud Computing
JournalACM International Conference Proceeding Series
PublisherACM Press
Web address (URL)http://dx.doi.org/10.1145/3018896.3036381
Journal citation(Article: a138), pp. 1 - 5
ISBN978-1-4503-4774-7
Web address (URL) of conference proceedingshttps://dl.acm.org/doi/proceedings/10.1145/3018896
Output statusPublished
Publication dates22 Mar 2017
Publication process dates
Deposited05 Jun 2023
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