Assessing model performance in Alzheimer's disease classification: The impact of data imbalance on fine-tuned vision transformers and CNN architectures

Problem : Data imbalance in medical datasets poses significant challenges for the performance of machine learning models, particularly in classifying Alzheimer’s disease (AD). Aim : This study aims to investigate the impact of the data ratio on model performance using both balanced and imbalanced datasets. Methods : We employed two distinct datasets: a balanced set of 34,000 images created through augmentation techniques and an inherently imbalanced set of 6,400 images, both comprising four classes. To evaluate model performance, we utilized three state-of-the-art models: fine-tuned vision transformer (FT-ViT), fine-tuned convolutional neural network (FT-CNN), and fine-tuned swin transformer (FT-Swin). Results : The FT-ViT model achieved an impressive 99% accuracy on the imbalanced dataset and 96% on the balanced dataset. The FT-CNN model attained 97% accuracy on the imbalanced dataset and 90% on the balanced dataset, while the FT-Swin model exhibited a performance disparity, achieving 79% accuracy on the balanced dataset and 90% on the imbalanced dataset. Conclusion : Our findings demonstrate that careful model selection, fine-tuning, and hyperparameter optimization can lead to high performance on imbalanced datasets without relying solely on artificial balancing methods. This approach offers promising implications for AD classification and potentially other medical imaging applications facing similar data imbalance challenges.