AML leukocyte classification method for small samples based on ACGAN

This study aims to assess the applicability of the auxiliary classification generative adversarial network (ACGAN) in the classification task for small samples of white blood cells. The method is trained on the TCIA dataset, and the classification accuracy is compared with two classical classifiers and the current state-of-the-art methods. The results are evaluated using accuracy, precision, recall, and F1 score. The accuracy of the ACGAN on the validation set is 97.1 % and the precision, recall, and F1 scores on the validation set are 97.5 , 97.3, and 97.4 %, respectively. In addition, ACGAN received a higher score in comparison with other advanced methods, which can indicate that it is competitive in classification accuracy.PMID:38547466 | DOI:10.1515/bmt-2024-0028
Source: Biomedizinische Technik/Biomedical Engineering - Category: Biomedical Engineering Authors: Source Type: research