MRI brain scan classification using novel 3-D statistical features
Conference paper
Authors | Hasan, A, Meziane, F., Aspin, R and Jalab, HA |
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Type | Conference 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.. |
Keywords | Linear discriminant analysis; ; Magnetic resonance imaging; ; Modified grey level co-occurrence matrix; ; Multi-layer perceptron neural network support vector machine |
Year | 2017 |
Conference | Second International Conference on Internet of Things, Data and Cloud Computing |
Journal | ACM International Conference Proceeding Series |
Publisher | ACM Press |
Web address (URL) | http://dx.doi.org/10.1145/3018896.3036381 |
Journal citation | (Article: a138), pp. 1 - 5 |
ISBN | 978-1-4503-4774-7 |
Web address (URL) of conference proceedings | https://dl.acm.org/doi/proceedings/10.1145/3018896 |
Output status | Published |
Publication dates | 22 Mar 2017 |
Publication process dates | |
Deposited | 05 Jun 2023 |
https://repository.derby.ac.uk/item/9z169/mri-brain-scan-classification-using-novel-3-d-statistical-features
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