PANN: an efficient parallel neural network based on the attentional mechanism for predicting Alzheimer's disease

Wenwen Bao; Huabin Wang; Xuejun Li; Xianjun Han; Gong Zhang

doi:10.1117/12.2623458

16 February 2022 PANN: an efficient parallel neural network based on the attentional mechanism for predicting Alzheimer's disease

Wenwen Bao, Huabin Wang, Xuejun Li, Xianjun Han, Gong Zhang

Author Affiliations +

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 120832B (2022) https://doi.org/10.1117/12.2623458
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

In recent years, machine learning methods have been extensively studied in Alzheimer's disease (AD) prediction. Most existing methods extract the handcraft features from images and then train a classifier for prediction. Although it has good performance, it has some deficiencies in essence, such as relying too much on image preprocessing, easily ignoring the latent lesion features. This paper proposes a deep learning network model based on the attention mechanism to learn the latent features of PET images for AD prediction. Firstly, we design a novel backbone network based on ResNet18 to capture the potential features of the lesion and avoid the problems of gradient disappearance and gradient explosion. Secondly, we add the channel attention mechanism so that the model can learn to use global information to selectively emphasize information features and suppress low-value features, which is conducive to the extraction of semantic features. Finally, we expand the data by horizontal flipping and random flipping, which reduces the over-fitting phenomenon caused by the limited medical data set and improves the generalization ability of the model. This method is evaluated on 238 brain PET images collected in the ADNI database, and the prediction accuracy is 94.2%, which is better than most mainstream algorithms.

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Wenwen Bao, Huabin Wang, Xuejun Li, Xianjun Han, and Gong Zhang "PANN: an efficient parallel neural network based on the attentional mechanism for predicting Alzheimer's disease", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 120832B (16 February 2022); https://doi.org/10.1117/12.2623458

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KEYWORDS

Data modeling

Brain

Feature extraction

Positron emission tomography

Magnetic resonance imaging

Alzheimer's disease

Performance modeling

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