Authors - Staphord Bengesi, Hoda El-Sayed, Md Kamruzzaman Sarker Abstract - Alzheimer's and Parkinson's diseases are two progressive neurodegenerative disorders that primarily affect senior citizens worldwide, and currently, there is no cure. In recent years, the number of diagnosed cases has been increasing. Since both diseases have an impact on the brain, MRI images are used as a crucial diagnostic tool. With advancements in AI, machine learning models are showing great promise in diagnosing and classifying MRI images. To explore this potential, we developed and tested five transformer models, such as ViT, Swin, DeiT, MedT, and Swin-ViT, using a Kaggle dataset containing MRI images from individuals with Alzheimer’s, Parkinson’s, and healthy controls. The models were evaluated on both a balanced dataset of over 2,900 samples and an unbalanced dataset of more than 7,000 samples. Our findings revealed that models trained on the unbalanced dataset outperformed those trained on the balanced dataset, highlighting the advantage of larger datasets in enhancing model performance.
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