CycleGAN-based Data Augmentation to Improve Generalizability Alzheimer’s Diagnosis using Deep Learning

Alzheimer's disease is a degenerative condition that progressively damages brain neurons, ultimately leading to dementia and death. Despite the limited number of available samples, effective diagnostic methods are crucial to diagnose Alzheimer's disease. Typically, a combination of laboratory and neuro-psychological testing is employed for diagnosis. The decrease in brain mass linked to Alzheimer's disease can be identified by MRI scans, which makes it a suitable problem for deep learning and computer vision. A precise and effective deep learning model would provide physicians with valuable support for their diagnoses. However, medical data is often challenging to obtain, and deep learning requires considerable data. To address this issue, generative adversarial networks can be useful. In this study, we proposed a CycleGAN to generate relevant synthetic images of intestinal parasites to solve the data scarcity challenge. To classify Alzheimer's disease using MRI scans, we developed convolutional neural networks based on the Google Inceptionv3 CNN architecture for this study. We attained an impressive F-1 score of 89%. Furthermore, we demonstrated the effectiveness of GANs in enhancing classification accuracy when used for data augmentation by creating samples with CycleGAN, achieving a remarkable F-1 score of 95%.