Ensemble deep learning for Alzheimer’s disease diagnosis using MRI: Integrating features from VGG16, MobileNet, and InceptionResNetV2 models

• Published in: PloS one Vol. 20; no. 4; p. e0318620
• Main Authors: Alruily, Meshrif, Abd El-Aziz, A A, Mostafa, Ayman Mohamed, Ezz, Mohamed, Mostafa, Elsayed, Alsayat, Ahmed, El-Ghany, Sameh Abd
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.04.2025
• Subjects: Accuracy; Aged; Algorithms; Alzheimer Disease - diagnosis; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Amyloid; Biology and Life Sciences; Brain - diagnostic imaging; Brain research; Classification; Cognitive ability; Computer and Information Sciences; Datasets; Deep Learning; Dementia; Dementia disorders; Diagnosis; Disease; Fatalities; Female; Humans; Image processing; Literature reviews; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Medicine and Health Sciences; Memory; Methods; Neural networks; Neurodegenerative diseases; Neurofibrillary tangles; Neuroimaging; Research and Analysis Methods; Senile plaques; Saudi Arabia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0318620

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain, leading to distinctive patterns of neuronal dysfunction and the cognitive decline emblematic of dementia. Currently, over 5 million individuals aged 65 and above are living with AD in the United States, a number projected to rise by 2050. Traditional diagnostic methods are fraught with challenges, including low accuracy and a significant propensity for misdiagnosis. In response to these diagnostic challenges, our study develops and evaluates an innovative deep learning (DL) ensemble model that integrates features from three pre-trained models—VGG16, MobileNet, and InceptionResNetV2—for the precise identification of AD markers from MRI scans. This approach aims to overcome the limitations of individual models in handling varying image shapes and textures, thereby improving diagnostic accuracy. The ultimate goal is to support primary radiologists by streamlining the diagnostic process, facilitating early detection, and enabling timely treatment of AD. Upon rigorous evaluation, our ensemble model demonstrated superior performance over contemporary classifiers, achieving a notable accuracy of 97.93%, along with a specificity of 98.04%, sensitivity of 95.89%, precision of 95.94%, and an F1-score of 87.50%. These results not only underscore the efficacy of the ensemble approach but also highlight the potential for DL to revolutionize AD diagnosis, offering a promising pathway to more accurate, early detection and intervention.