A COMPARISON OF DATA BALANCING METHODS FOR MEDICAL DATA
Abstract
Class imbalance is a common issue in medical datasets and affects the performance of deep learning models in medical diagnosis. This study aims to evaluate different data balancing methods for medical imaging classification tasks. Experiments were conducted on two datasets, chest X-rays for pneumonia detection and brain MRI scans for tumor classification. Traditional resampling methods (downsampling and oversampling), advanced oversampling techniques (SMOTE, ADAYSN), and Generative Adversarial Networks (GANs) were implemented and compared. Performance metrics including recall, F1-score, ROCAUC, and G-Mean were used to assess the effectiveness of each method. Our results demonstrate that GAN-based approaches consistently outperform traditional techniques across various evaluation metrics, with notable improvements in F1-score (2.95 - 52.4%), ROC-AUC (7.93 - 19.5%), and geometric mean (6.28 - 36.3%). This study provides valuable insights for researchers and practitioners seeking to improve diagnostic models trained on imbalanced medical datasets.
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