Advancements in Neuroimaging for Dementia Diagnosis

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Abstract

A wide range of cognitive impairments are collectively referred to as dementia, and diagnosing dementia can be difficult, especially when dealing with disorders such as Parkinson's, Alzheimer's, and frontotemporal dementia. This study explores the application of Artificial Intelligence (AI), specifically Deep Convolutional Neural Networks (DCNNs) like VGG-16 and ResNet50. Our novel model combines the strengths of VGG-16 and ResNet50, and it achieves an impressive 96% accuracy rate in differentiating between brains affected by dementia and brains that are healthy, based on neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Parkinson's Progression Markers Initiative (PPMI). Our study highlights how artificial intelligence (AI) can revolutionize conventional diagnostic techniques by providing a novel way for pattern recognition in neuroimaging. We rigorously test our VGG-16 and ResNet50 hybrid model on a variety of datasets to reduce overfitting and ensure its robustness and generalizability. Furthermore, we investigate the novel use of Generative Adversarial Networks (GANs) for dementia diagnosis, presenting Artificial Neurological Networks (AANs) that model the neurological patterns of individuals with dementia. GAN-based models show great potential for improving datasets and resolving the problems of imbalance and scarcity in neuroimaging data, despite their initial 60% accuracy. This research underscores the transformative potential of AI to revolutionize healthcare while highlighting the advances in neuroimaging for dementia diagnosis. In order to advance precision medicine in dementia care and improve patient outcomes, AI-driven diagnostic tools are essential. These technologies enable the early detection and precise treatment of neurodegenerative illnesses.

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Keywords

Convolutional Neural Network; GAN; Neurodegenerative ; ResNet50; VGG16.