Alzheimer's Disease Evaluation Through Visual Explainability by Means of Convolutional Neural Networks.
Int J Neural Syst
; 34(2): 2450007, 2024 Feb.
Article
de En
| MEDLINE
| ID: mdl-38273799
ABSTRACT
Background and Objective:
Alzheimer's disease is nowadays the most common cause of dementia. It is a degenerative neurological pathology affecting the brain, progressively leading the patient to a state of total dependence, thus creating a very complex and difficult situation for the family that has to assist him/her. Early diagnosis is a primary objective and constitutes the hope of being able to intervene in the development phase of the disease.Methods:
In this paper, a method to automatically detect the presence of Alzheimer's disease, by exploiting deep learning, is proposed. Five different convolutional neural networks are considered ALEX_NET, VGG16, FAB_CONVNET, STANDARD_CNN and FCNN. The first two networks are state-of-the-art models, while the last three are designed by authors. We classify brain images into one of the following classes non-demented, very mild demented and mild demented. Moreover, we highlight on the image the areas symptomatic of Alzheimer presence, thus providing a visual explanation behind the model diagnosis.Results:
The experimental analysis, conducted on more than 6000 magnetic resonance images, demonstrated the effectiveness of the proposed neural networks in the comparison with the state-of-the-art models in Alzheimer's disease diagnosis and localization. The best results in terms of metrics are the best with STANDARD_CNN and FCNN with accuracy, precision and recall between 98% and 95%. Excellent results also from a qualitative point of view are obtained with the Grad-CAM for localization and visual explainability.Conclusions:
The analysis of the heatmaps produced by the Grad-CAM algorithm shows that in almost all cases the heatmaps highlight regions such as ventricles and cerebral cortex. Future work will focus on the realization of a network capable of analyzing the three anatomical views simultaneously.Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Maladie d'Alzheimer
Type d'étude:
Qualitative_research
/
Screening_studies
Limites:
Female
/
Humans
/
Male
Langue:
En
Journal:
Int J Neural Syst
Sujet du journal:
ENGENHARIA BIOMEDICA
/
INFORMATICA MEDICA
Année:
2024
Type de document:
Article
Pays d'affiliation:
Italie
Pays de publication:
Singapour