Deep learning model for differentiating acute myeloid and lymphoblastic leukemia in peripheral blood cell images via myeloblast and lymphoblast classification.
Sholhui ParkYoung Hoon ParkJungwon HuhSeung Min BaikDong-Jin ParkPublished in: Digital health (2024)
The performance of the developed ensemble model for the 12 cell classifications was satisfactory, particularly for myeloblasts and lymphoblasts. We believe that the application of our model will benefit healthcare settings where the rapid and accurate diagnosis of AL is difficult.
Keyphrases
- deep learning
- healthcare
- peripheral blood
- convolutional neural network
- single cell
- acute myeloid leukemia
- bone marrow
- machine learning
- cell therapy
- artificial intelligence
- liver failure
- magnetic resonance imaging
- high resolution
- magnetic resonance
- respiratory failure
- mesenchymal stem cells
- intensive care unit
- optical coherence tomography
- social media
- mechanical ventilation
- loop mediated isothermal amplification