Publication

Abstract : Alzheimer’s Disease (AD) is a common neurological brain disorder that causes the brain cells to die and shrink (Atrophy) gradually, resulting in a continuous decline in one’s ability to function independently. Early diagnosis increases the possibility of preventing or delaying the advancement of this mental disorder. Magnetic Resonance Imaging (MRI) offers the potential of non-invasive longitudinal monitoring and plays a vital role as a biomarker of the disease progression. Structural Magnetic Resonance Imaging (sMRI) helps to measure Atrophy, which is considered to be the most dependable biomarker to assess the exact stage and severity of the neuro-degenerative aspect of AD pathology. There are five stages associated with AD, which include Normal Control (NC), Early Mild Cognitive Impairment (EMCI), Mild Cognitive Impairment (MCI), Late Mild Cognitive Impairment (LMCI), and Alzheimer’s Disease (AD). In this work, we have used the Alzheimer’s Disease Neuroimaging Initiative (ADNI2) sMRI image dataset to measure and classify the stage of AD. In recent years, Convolutional Neural Networks (CNNs) are widely used for medical image analysis. This work focuses on applying different Deep Learning algorithms for the multi-class classification of AD MRI images and proposes the best pre-trained model that can accurately predict the patient’s stage. It is observed that ResNet-50v2 gives the best accuracy of 91.84% and f1-score of 0.97 for AD class. Visualization techniques such as Grad-CAM and Saliency Map are applied on the model that gave the best accuracy to understand the region of focus in the image which led to predicting its class.