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

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.