A Novel Approach Towards Early Detection of Alzheimer’s Disease Using Deep Learning on Magnetic Resonance Images

Magnetic Resonance Imaging (MRI) is used extensively for the diagnosis of Alzheimer’s Disease (AD). Early detection of AD can help people with early intervention and alleviate the progression of disease symptoms. Previous studies have applied deep learning methods for computer-aided diagnosis of AD. In this present study, an efficient architecture has been proposed, composed of a 2D Convolutional neural network with batch normalization for the classification of AD using MRI images. The proposed model was created using 11 layers, which was obtained by experimenting with different combinations of batch normalization and activation functions. All the experiments are performed using the Alzheimer Disease Neuroimaging Initiative (ADNI) data. The novelty of our approach was that different slices of the brain, such as axial, coronal, and sagittal, were used to classify brain slices into three classes: Cognitively Normal (NC), Mild Cognitive Impairment (MCI), and AD. The proposed model achieved a sensitivity (SEN) of 99.73% for NC, 99.79% for MCI, and 99.96% for AD, a specificity (SPE) of 99.80% for NC, 99.90% for MCI, and 99.74% for AD, and accuracy of 99.82%. The contribution of our proposed method’s classification accuracy was better than that of the recent state-of-the-art methods.