We present a new automated system for the detection of brain magnetic resonance images (MRI) affected by Alzheimer's disease (AD). The MRI is analyzed by means of multiscale analysis (MSA) to obtain its fractals at six different scales. The extracted fractals are used as features to differentiate healthy brain MRI from those of AD by a support vector machine (SVM) classifier. The result of classifying 93 brain MRIs consisting of 51 images of healthy brains and 42 of brains affected by AD, using leave-one-out cross-validation method, yielded 99.18% ± 0.01 classification accuracy, 100% sensitivity, and 98.20% ± 0.02 specificity. These results and a processing time of 5.64 seconds indicate that the proposed approach may be an efficient diagnostic aid for radiologists in the screening for AD.
Keyphrases
- deep learning
- magnetic resonance
- contrast enhanced
- resting state
- white matter
- magnetic resonance imaging
- convolutional neural network
- functional connectivity
- artificial intelligence
- machine learning
- cerebral ischemia
- optical coherence tomography
- diffusion weighted imaging
- cognitive decline
- real time pcr
- single cell