GAN-Enhanced Deep Learning for Improved Alzheimer's Disease Classification and Longitudinal Brain Change Analysis

Purushottam Pandey¹Surbhi Bhatia Khan^2*Jyoti Pruthi¹Eid Albalawi³Ali Algarni⁴Ahlam Almusharraf⁵

• ¹Manav Rachna International Institute of Research and Studies (MRIIRS), Faridabad, Haryana, India
• ²University of Salford, Salford, United Kingdom
• ³King Faisal University, Al-Ahsa, Eastern Province, Saudi Arabia
• ⁴King Khalid University, Abha, Saudi Arabia
• ⁵Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Alzheimer is defined by a progressive decline in cognitive functions and memory. Early detection is crucial to mitigate the devastating impacts, which can significantly impair a person's quality of life. Traditional methods while still in use, is time-consuming and inefficient. Advancements in AI, specifically machine learning and deep learning, offer promising solutions to these challenges. AI techniques can process large datasets with high accuracy, significantly improving the speed and precision of AD detection. However, despite these advancements, issues like limited accuracy, computational complexity, and the risk of overfitting still pose challenges in the field of AD classification. To address these challenges, the proposed work integrates deep learning architectures, particularly ResNet101 and LSTM networks, to enhance both feature extraction and classification of AD. The ResNet101 model is augmented with innovative layers such as the PDPO and the DCK, which are designed to extract the most relevant features from datasets like ADNI and OASIS. These features are then processed through the LSTM model, which classifies individuals into categories like Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD). Another key aspect of the research is the use of Generative Adversarial Networks (GANs) to identify the progressive or non-progressive nature of AD. By employing both a generator and a discriminator, the GAN model detects whether the AD state is advancing. If the original and predicted classes align, AD is deemed non-progressive; if they differ, the disease is progressing. This innovative approach provides a nuanced view of AD, which could lead to more precise and personalized treatment plans. The numerical outcome obtained by the proposed model for ADNI dataset is 0.9931 and for OASIS dataset the accuracy gained by the model is 0.9985. Ultimately, this research aims to offer significant contributions to the medical field, helping healthcare professionals diagnose AD more accurately and efficiently, thus improving patient outcomes. Furthermore, brain simulation models are integrated into this framework to provide deeper insights into the underlying neural mechanisms of AD. These brain simulation models help visualize and predict how AD may evolve in different regions of the brain, enhancing both diagnosis and treatment planning.