Data augmentation with improved regularisation and sampling for imbalanced blood cell image classification.

ABSTRACT: Due to progression in cell-cycle or duration of storage, classification of morphological changes in human blood cells is important for correct and effective clinical decisions. Automated classification systems help avoid subjective outcomes and are more efficient. Deep learning and more specifically Convolutional Neural Networks have achieved state-of-the-art performance on various biomedical image classification problems. However, real-world data often suffers from the data imbalance problem, owing to which the trained classifier is biased towards the majority classes and does not perform well on the minority classes. This study presents an imbalanced blood cells classification method that utilises Wasserstein divergence GAN, mixup and novel nonlinear mixup for data augmentation to achiev

SUBMITTER: Rana P 

PROVIDER: S-EPMC9613648 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature