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Background and Objective: Early diagnosis of Alzheimer's disease (AD) seems necessary due to the high cost of care and treatment, the uncertainty of existing therapies, as well as the worrying future of the patient. This study was conducted to AD diagnosis by MRI images using artificial intelligence methods.
Methods: In this research, a computer system for early detection of AD with using machine learning algorithms is presented in the framework of computer-aided process. Conditional random field and Inception deep neural network have been adapted for brain MR images to detect AD. Since hippocampal tissue is one of the first tissues to be affected by AD, hence for the early detection of this disease, the hippocampus was located from other brain tissues firstly and then due to the extent to which this tissue is affected, the diagnosis was made. Conditional random field could accurately extract hippocampal fragments of different shapes in all three brain planes. These components serve as the basis for feature extraction by the deep network. The proposed method was tested on standard ADNI dataset images and its performance was demonstrated. The used Inception network has been trained on the huge ImageNet dataset. One of the important steps is knowledge transfer of the problem under consideration. To facilitate this, data augmentation process was applied according to the shape and structure of the hippocampus.
Results: The implemented method in this research, achieved to 98.51% accuracy for two-class classification of "Alzheimer" versus "Normal control" and achieved to 93.41% accuracy for two-class classification of "Mild cognitive impairment" versus "Normal control", which increased by 2.56% and 8.41%, compared with the rival methods, respectively.
Conclusion: The achieved results of this study showed that the using of artificial intelligence techniques has highly accurate in diagnosing AD according to MRI images.