Abstract

Alzheimer’s disease (AD) is a neurological condition that causes memory loss and cognitive impairment and is gradual and irreversible. Timely intervention and a better quality of life for a patient are possible only through early and accurate diagnosis. This paper seeks to model abnormal neural circuits with brain networks that forecast a sound and timely detection of AD via cutting-edge neuroimaging methods. The Decoupling Generative Adversarial Network (DecGAN) is suggested to identify aberrant neural networks associated with AD. A decoupling module in the model separates brain networks into (i) a sparse network of the brain that contains circuits of great significance, and (ii) an additional network of trivial disease contribution. A conflicted learning scheme guarantees the emphasis on the features that are related to the disease, whereas a sparse capacity loss operation maintains the inherent topographical arrangement of neural networks. The model is trained and tested on DTI and rs-fMRI data of ADNI. Performance is evaluated using ROC-AUC, accuracy, precision, recall, F1-score, and training & validation loss. The proposed DecGAN had 92.1% accuracy, 89.2% precision, 86.5% recall, and 87.1% F1-score, with a ROC-AUC of 93.8% and a final validation loss of 0.0040 and it was significantly better than the current baseline and advanced classification methods. Superior discriminative performance for early-stage AD detection is indicated by a higher ROC-AUC, while better convergence, greater generalization, and less overfitting are demonstrated by lower validation loss. In this work, a sparse capacity loss function that maintains the neural circuit’s topological distribution during decoupled graph reconstruction is introduced for the first time. Proposed methodology allows robust detection of AD-related aberrant circuits even under moderate changes in brain network structure by explicitly restricting sparsity and topology combined. Previous AD classification approaches based on GANs were unable to capture this capability.

Authors

P. Dinesh Kumar
Kodaikanal International School, India

Keywords

Alzheimer’s Disease, Generative Adversarial Network, Deep Learning (DL), Magnetic Resonance Images (MRI), Random Forest