Diagnosis and Prognosis of Alzheimer’s Disease Via 3D CNN

Abstract

Mild Cognitive Impairment (MCI) is an early symptom of Alzheimer’s disease (AD). The feature extraction and deep learning architecture of the convolutional neural network in 3D brain images is applied to the problem of Alzheimer’s disease. The Structural Magnetic Resonance (sMRI) and Positron Emission Tomography (PET) image of the patient’s brain are classified according to the vigorousness of the disease and is labelled to be either in MCI or in AD or Normal Control (NC) condition. In this paper, we proposed a model and presented the baseline convolutional CNN with four layers viz., Convolutional layer, Leaky Rectified Linear Unit(LReLU), S3Pool layer and Global average pooling. Further, the 3D image data is used to perform the binary and ternary classifications and its performance are examined. The strength of the network has improved interior resource utilization evaluated with medical images, sMRI and PET on hippocampal ROI. The results of our proposed CNN architecture have achieved an accuracy level of 0.945, 0.859 and 0.748 respectively, when compared to the conventional AlexNet based network. The obtained data from the ADNI database shows better performance with our proposed model.