Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images
This work addresses early detection of Alzheimer's disease for patients and clinicians, but it is incremental as it applies existing ensemble methods to a known dataset.
The study tackled the classification of Alzheimer's disease into four groups using PET scan images from the ADNI dataset, achieving an overall average accuracy of 93.13% and an AUC of 94.4% through an ensemble of deep-learning models.
Alzheimer's disease is a progressive neurodegenerative disorder that primarily affects cognitive functions such as memory, thinking, and behavior. In this disease, there is a critical phase, mild cognitive impairment, that is really important to be diagnosed early since some patients with progressive MCI will develop the disease. This study delves into the challenging task of classifying Alzheimer's disease into four distinct groups: control normal (CN), progressive mild cognitive impairment (pMCI), stable mild cognitive impairment (sMCI), and Alzheimer's disease (AD). This classification is based on a thorough examination of PET scan images obtained from the ADNI dataset, which provides a thorough understanding of the disease's progression. Several deep-learning and traditional machine-learning models have been used to detect Alzheimer's disease. In this paper, three deep-learning models, namely VGG16 and AlexNet, and a custom Convolutional neural network (CNN) with 8-fold cross-validation have been used for classification. Finally, an ensemble technique is used to improve the overall result of these models. The results show that using deep-learning models to tell the difference between MCI patients gives an overall average accuracy of 93.13% and an AUC of 94.4%.