An Ensemble Learning Approach for Early Detection and Prediction of Alzheimer’s Disease
DOI:
https://doi.org/10.5281/zenodo.21817261Keywords:
Alzheimer’s Disease, Medical Image processing, Neural Networks, Machine Learning, CNN and RNNAbstract
Alzheimer’s disease is a kind of brain detrimental ailment that affects one person for every three second all over the globe. Therefore, detection and prediction of Alzheimer’s disease is a crucial task to initiate the recovery or sustainability proceedings. Early detection of Alzheimer’s disease is proven to provide better healing process. Image processing techniques such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are widely used in the process of Alzheimer’s disease detection and prediction. Manual inspection of these images is a time-consuming complex task in general. There are some computerized detection and prediction methods are in practice to mitigate the complexity of this process. This work is indented to introduce an ensemble of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) for Alzheimer’s detection and prediction process with higher accuracy and Precision. An enhanced preprocessing method is introduced in this work along with Optimized Kernel Queue for CNN and the Ensemble of CNN-RNN functional modules.
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