Early diagnosis of Alzheime`s disease based on VGG19 feature extractor and MRI analysis

Abstract

Alzheimer’s Disease (AD) is the most prevalent cause of dementia, a neurodegenerative\ndisorder primarily affecting the brain. Mild cases of dementia gradually worsen, resulting in a\ndecline in cognitive abilities, including memory, thinking, reasoning, and behavior. Currently,\nthere is no cure for AD; however, early diagnosis of mild cases can help slow the disease\'s\nprogression, improve its management, and enhance overall quality of life. Existing models for\nearly AD diagnosis encounter challenges, such as low accuracy and precision, poor\ngeneralization and overfitting due to data imbalances in datasets. To address these limitations, we\npropose a novel Deep Learning (DL) based model for early AD diagnosis. Our model comprises\nthree main components: image processing and data augmentation, feature extraction based on\nVGG19 (Visual Geometry Group 19), and a Multi-Layer Perceptron (MLP) based classifier\ncategorizing input Magnetic Resonance Imaging (MRI) scans into four classes representing AD\'s\ndevelopmental stages: Very-Mild-Demented, Mild-Demented, Moderate-Demented, and NonDemented. We optimized our model using the ADAM Algorithm and applied it to images selected\nfrom the Open Access Series of Imaging Studies (OASIS) dataset. Our model yielded impressive\nresults, achieving an accuracy of 92.58%, an AUC of 99%, and precision, recall, and F-measure\nvalues of 0.907, 0.894, and 0.890, respectively. Furthermore, we conducted a comparative\nanalysis with several state-of-the-art models, consistently demonstrating that our proposed model\noutperforms them.