Brain MRI Image Analysis for Alzheimer’s Disease Diagnosis Using Mask R-CNN
Keywords:
Mask R-CNN, MRI, Alzheimer's disease, Deep Learning, Convolutional Neural Network (CNN), Brain ImagingAbstract
The most prevalent kind of dementia and the fifth-leading cause of mortality for those over 65 is Alzheimer's disease. Furthermore, in accordance with governmental statistics, there has been a significant increase in the number of fatalities attributed to Alzheimer's disease. Therefore, the early detection of Alzheimer's disease holds the potential to enhance the likelihood of survival for affected patients. Magnetic Resonance Imaging (MRI) combined with machine learning methods has facilitated and expedited the diagnosis of Alzheimer's disease. However, utilizing handmade feature extraction methods on MRI images with traditional machine learning approaches is challenging and needs the assistance of a knowledgeable user. Therefore, a technique might automate the process and reduce the necessity for feature extraction by utilizing deep learning as an automatic recognition and feature extraction. In this research, we use Mask-RCNN, a convolutional neural network approach, to demonstrate, can be used to the segmentation and object recognition of 40 moderately demented and non-demented MRI images from the train and test datasets. MRI image object recognition and object instance segmentation were the initial applications for Mask-RCNN. With this experiment, we demonstrate that Mask R-CNN is applicable and best to the treatment of Alzheimer’s disease of brain from MRI images, with an accuracy of up to 97.46%.
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