Enhancing Alzheimer's Disease Diagnosis through Magnetic Resonance Imaging: An Analysis using VGG19 Architectures

Abstract

Early detection of Alzheimer's disease (AD) is an area of much research since early diagnosis can offer patient better treatment and enhanced care. In this work we propose a deep learning approach to detect Alzheimer’s disease using the VGG-19 architecture, one of the state-of-the-art convolutional neural networks (CNN). In this work we utilized a dataset composed of a heterogeneous set of brain MRI images from healthy subjects and Alzheimer patients, they are part of the ADNI (Alzheimer's Disease Neuroimaging Initiative). The dataset was preprocessed with a few techniques such as image normalization, augmentation, and denoising to further increase the model's performance. These techniques also expanded the quality of the input data which, coupled with an impressive state-of-the art classification accuracy of 97 %, helped to achieve these results. The results showed deep learning can be effective for early detection of Alzheimer’s disease as a useful clinical diagnostic tool. Finally, this work demonstrates how CNNs such as VGG 19 are ready to be used in medical image analysis and renders a new benchmark on the accuracy of Alzheimer detection.