Cycle GAN-Based MRI Augmentation and Hybrid Deep Learning Approach (GDP) for Binary Classification of Alzheimer's Disease: Differentiating Normal and Very Mild Dementia

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects older adults, leading to memory loss, cognitive decline, and behavioural changes. It is marked by the accumulation of amyloid-beta plaques and tau tangles in the brain, which impair neuronal function and eventually cause cell death. Diagnosis typically involves clinical evaluations, cognitive assessments, and imaging techniques such as MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) scans. Early detection is crucial, as it allows for interventions that can potentially slow the disease's progression, enabling patients and their families to manage symptoms more effectively. The transition from Normal Control (NC) to Early Mild Cognitive Impairment (EMCI) may be reversible, whereas other stages are not. This makes it a particularly challenging classification problem, as the changes in biomarker values are typically minimal during the progression from NC to very mild stages. To address this issue, we created a hybrid deep learning model, GDP (combining GoogLeNet, DenseNet-121, and Principal Component Analysis (PCA)), to extract hybrid features using 26639 sMRI images from well-known MRI datasets, such as the Open Access Series of Imaging Studies (OASIS) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). These features fed into the SVM binary classification algorithm to distinguish very mild cases from healthy images. We created synthetic images using CycleGAN augmentation to balance the dataset across labels and expand its size. The proposed GDP model stands out from current state-of-the-art deep learning techniques, such as ResNet- 50, DenseNet-169, VGG-16, EfficientNet, and Inceptionv3, by achieving an impressive accuracy of 98.98% and an AUC of 99.91%. These results significantly surpass the performance of existing methods, making it a promising tool for early Alzheimer's disease prediction.