Prediction of Alzheimer's disease Using Stacked Ensemble Transfer Neural Network Model
Keywords:
Brain imaging; convolutional neural network (CNN); Alzheimer’s disease; pre-trained model; Ensemble learningAbstract
It is studied that Alzheimer's disease (AD) is growing fast and main cause of the death for the elderly people. Early detection of Alzheimer's disease has been proven to enhance patient outcomes. Machine learning techniques that utilize magnetic resonance imaging (MRI) have been used for AD diagnosis, but traditional methods require manual feature extraction by an expert, which can be complex. To address this, our study proposes a new approach using a pre-trained convolutional neural network called Stacked Ensemble Transferred Neural Network (SETNN) model for automated features extraction when using MRI images to detect Alzheimer's disease. The effectiveness of the SETNN model was assessed using a number of criteria, including accuracy, in comparison to traditional Softmax and support vector machine (SVM) techniques. The outcomes shown that, when applied to the MRI images from the ADNI dataset, the proposed SETNN model outperformed existing state-of-the-art models, achieving 99.49% accuracy. The developed model shall improve the prediction efficiency and decision making with early detection of Alzheimer.
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