Name
Abstract | ||
|---|---|---|
The paper presents an automated algorithm for detecting and classifying MRI brain slices into normal and abnormal based on a novel three-dimensional modified grey level co-occurrence matrix. This approach is used to analyze and measure asymmetry between the two brain hemispheres. The experimental results demonstrate the efficacy of 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. |
Year | Venue | Field |
|---|---|---|
2017 | ICC | Multi layer perceptron neural network,Pattern recognition,Computer science,Support vector machine,Computer network,Artificial intelligence,Linear discriminant analysis,Neuroimaging,Artificial neural network,Perceptron,Machine learning,Magnetic resonance imaging |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali M. Hasan | 1 | 0 | 0.34 |
| Farid Meziane | 2 | 308 | 37.98 |
| Rob Aspin | 3 | 97 | 12.40 |
| Hamid A. Jalab | 4 | 144 | 23.33 |