Alzheimer’s Disease Image Classification Based on Improved EfficientNet

doi:10.3969/j.issn.1006-2475.2023.06.010

Abstract

Abstract: To improve the effectiveness of the convolutional neural network for Alzheimer’s disease MRI image classification, a convolutional neural network FAMENET is proposed, which integrates an adaptive attention mechanism and data enhancement technique to alleviate data imbalance by introducing a data augmentation technique and Focal Loss loss function. The network is reconfigured to reduce the number of model parameters and the computational effort of the network while maintaining accuracy. The adaptive attention mechanism is introduced to solve the information loss problem caused by the downsampling of input images for feature extraction. In a large number of comparative experiments on public datasets, the classification accuracy of FAMENET reaches 79.95% and the AUC value reaches 82.54%. The designed ablation experiments also fully demonstrate the effectiveness of the proposed modules and networks.

Key words: convolutional neural network, Alzheimer’s Disease, convolutional block attention module, data augmentation, medical image classification

CLC Number:

TP183

ZHU Jian-bo, GE Ming-feng, DONG Wen-fei. Alzheimer’s Disease Image Classification Based on Improved EfficientNet[J]. Computer and Modernization, 2023, 0(06): 56-61.

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