A Multistage CNN with Branch Concatenation for Classification of Dementia Using MRI Data

Abstract

Alzheimer’s Disease (AD) is the most common type of dementia and is caused by the accumulation of amyloid-beta plaques in the brain. Worldwide cases of dementia are expected to triple by 2050, which underscores the importance of early diagnosis. In our work, we proposed a multibranched CNN with three concatenations among the branches and tested the method on a dataset accessed from Kaggle. We also implement the SMOTE algorithm on the dataset to overcome class imbalance. The proposed CNN achieved 99.64% accuracy and 99.89% F1-Score on test data and outperformed the various existing methods. The proposed architecture is special because of its ability to extract intricate features at finer levels. The research paves the pathway for improved treatment plans and better prognosis of AD.