Transformative Insights: Swin Transformer's Impact on Alzheimer's Detection
Keywords:
Alzheimer’s disease, Neurodegenerative diseases, Patch Embedding, Swin Transformer.Abstract
An irreversible neurological illness, Alzheimer's disease (AD) causes a rapid loss in mental function. It is primarily caused by a widespread degeneration of the brain cell and can strike at any age. Due to the progression of Alzheimer's disease, there is a great clinical, societal, and financial demand for early detection of AD. With neural network topologies, deep learning (DL) has become a potent tool for disease categorization, automatically deriving complex patterns from medical data. Deep learning models are useful for finding complex diseases because of their capacity to recognize minute patterns and connections. The major purpose of this work is to overcome the AD diagnosis challenges via MRI images of the brain by constructing a unique deep learning-based model employing a Swin Transformer. The Kaggle repository offers a well-curated and extensive dataset that is an invaluable tool for doing in-depth analysis and obtaining significant insights. The performance of the Swin Transformer study is demonstrated by its outstanding overall accuracy of 95.12%. Furthermore, a thorough investigation of the performance of current methods at the class level has been carried out by analyzing the parameters of the confusion matrix.
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