Image-Based Alzheimer's Disease Detection Using Pretrained Convolutional Neural Network Models

Abstract

Alzheimer's disease is an untreatable, progressive brain disorder that slowly robs people of their memory, thinking abilities, and ultimately their capacity to complete even the most basic tasks. Among older adults, it is the most frequent cause of dementia. Although there is presently no treatment for Alzheimer's disease, scientific trials are ongoing to discover drugs to combat the condition. Treatments to slow the signs of dementia are also available. Many researchers throughout the world became interested in developing computer-aided diagnosis systems to aid in the early identification of this deadly disease and assure an accurate diagnosis. In particular, image-based approaches have been coupled with machine learning techniques to address the challenges of Alzheimer's disease detection. This study proposes a computer-aided diagnosis system to detect Alzheimer's disease from biomarkers captured using neuroimaging techniques. The proposed approach relies on deep learning techniques to extract the relevant visual features from the image collection to accurately predict the Alzheimer's class value. In the experiments, standard datasets and pre-trained deep learning models were investigated. Moreover, standard performance measures were used to assess the models' performances. The obtained results proved that VGG16-based models outperform the state-ofthe-art performance.

Adversarial Network-Based Classification for Alzheimer’s Disease Using Multimodal Brain Images: A Critical Analysis

“…N. Alsadhan et al [14] proposed a CNN-based VGG16 and ResNet 50 model for AD detection. The proposed method extracted visual information from the image data using DL techniques, which accurately predicted the AD class value.…”

“…Meanwhile, ADNI [1], [45], [47], and the Open Access Series of Imaging Studies (OASIS) [36], [37] stand out as significant contributors to advancing research in this domain. Structural MRI [2], [18], [27], [32], [33], [36], [39] OASIS MRI MRI images with a focus on brain tumor detection Structural MRI [5], [8], [14], [15], [17], [19], [21], [22], [24], [25], [36], [37], [38], [42], [43] MRI ADNI Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset Functional and Structural MRI, PET [1], [4], [4], [7], [16], [23], [28], [29], [30], [40], [41], [44], [45] ADNI GARD Gwangju Alzheimer's and Related Dementia (GARD) dataset…”