A minimalistic approach to classifying Alzheimer's disease using simple and extremely small convolutional neural networks.
J Neurosci Methods
; 411: 110253, 2024 Nov.
Article
in En
| MEDLINE
| ID: mdl-39168252
ABSTRACT
BACKGROUND:
There is a broad interest in deploying deep learning-based classification algorithms to identify individuals with Alzheimer's disease (AD) from healthy controls (HC) based on neuroimaging data, such as T1-weighted Magnetic Resonance Imaging (MRI). The goal of the current study is to investigate whether modern, flexible architectures such as EfficientNet provide any performance boost over more standard architectures.METHODS:
MRI data was sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and processed with a minimal preprocessing pipeline. Among the various architectures tested, the minimal 3D convolutional neural network SFCN stood out, composed solely of 3x3x3 convolution, batch normalization, ReLU, and max-pooling. We also examined the influence of scale on performance, testing SFCN versions with trainable parameters ranging from 720 up to 2.9 million.RESULTS:
SFCN achieves a test ROC AUC of 96.0% while EfficientNet got an ROC AUC of 94.9 %. SFCN retained high performance down to 720 trainable parameters, achieving an ROC AUC of 91.4%. COMPARISON WITH EXISTINGMETHODS:
The SFCN is compared to DenseNet and EfficientNet as well as the results of other publications in the field.CONCLUSIONS:
The results indicate that using the minimal 3D convolutional neural network SFCN with a minimal preprocessing pipeline can achieve competitive performance in AD classification, challenging the necessity of employing more complex architectures with a larger number of parameters. This finding supports the efficiency of simpler deep learning models for neuroimaging-based AD diagnosis, potentially aiding in better understanding and diagnosing Alzheimer's disease.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Magnetic Resonance Imaging
/
Neural Networks, Computer
/
Alzheimer Disease
/
Neuroimaging
Limits:
Aged
/
Aged80
/
Female
/
Humans
/
Male
Language:
En
Journal:
J Neurosci Methods
Year:
2024
Document type:
Article
Country of publication:
Netherlands