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RESEARCH PRODUCT

Deep 3D Convolution Neural Network for Alzheimer’s Detection

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One of the most well-known and complex applications of artificial intelligence (AI) is Alzheimer’s detection, which lies in the field of medical imaging. The complexity in this task lies in the three-dimensional structure of the MRI scan images. In this paper, we propose to use 3D Convolutional Neural Networks (3D-CNN) for Alzheimer’s detection. 3D-CNNs have been a popular choice for this task. The novelty in our paper lies in the fact that we use a deeper 3D-CNN consisting of 10 layers. Also, with effectively training our model consisting of Batch Normalization layers that provide a regularizing effect, we don’t have to use any transfer learning. We also use the simple data augmentation technique of flipping. Our model is trained for binary classification that distinguishes between Alzheimer’s and normal, as well as multiclass classification consisting of Alzheimer’s, Mild Cognitive Impairment, and normal classes. We tested our model on the ADNI dataset and achieved 94.17% and 89.14% accuracy for binary classification and multiclass classification, respectively.