GAN-enhanced deep learning for improved Alzheimer's disease classification and longitudinal brain change analysis

Alzheimer's disease (AD) is commonly defined by a progressive decline in cognitive functions and memory. Early detection is crucial to mitigate the devastating impacts of AD, which can significantly impair a person's quality of life. Traditional methods for diagnosing AD, while still in use, often involve time-consuming processes that are prone to errors and inefficiencies. These manual techniques are limited in their ability to handle the vast amount of data associated with the disease, leading to slower diagnosis and potential misclassification. Advancements in artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), 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 such as limited accuracy, computational complexity, and the risk of overfitting still pose challenges in the field of AD classification. To address these challenges, the proposed study integrates deep learning architectures, particularly ResNet101 and long short-term memory (LSTM) networks, to enhance both feature extraction and classification of AD. The ResNet101 model is augmented with innovative layers such as the pattern descriptor parsing operation (PDPO) and the detection convolutional kernel layer (DCK), which are designed to extract the most relevant features from datasets such as ADNI and OASIS. These features are then processed through the LSTM model, which classifies individuals into categories such as 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.