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

TL;DR

This paper presents a deep learning-based computer-aided diagnosis system using pretrained CNN models, specifically VGG16, to detect Alzheimer's disease from neuroimaging data, achieving superior performance over existing methods.

Contribution

It introduces a novel application of pretrained CNN models for Alzheimer's detection from neuroimages, demonstrating improved accuracy over previous approaches.

Findings

VGG16-based models outperform other deep learning models.

Pretrained models effectively extract relevant features for diagnosis.

Standard datasets and metrics validate the approach's effectiveness.

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 of the art performance.