Abstract
The incidence of acute myeloid leukemia (AML) is increasing annually, and timely diagnostic and treatments can substantially improve patient survival rates. AML typing traditionally relies on manual microscopy for classifying and counting myeloid cells, which is time-consuming, laborious, and subjective. Therefore, developing a reliable automated model for myeloid cell classification is imperative. This study evaluated the performance of five widely-used classification models on the largest publicly available bone marrow cell dataset (BM). However, the accuracy of the classification model is significantly affected by the imbalance in the distribution of bone marrow cell types. To address this issue, this study analyzed five different Loss functions and seven different attention mechanisms. When the classification models is chosen, Swin Transformer V2 was found to perform the best. However, the lightweight model RegNetX-3.2gf had significantly fewer parameters and a significantly faster inference speed than Swin Transformer V2, and its F1 Score was only 0.032 lower than that of Swin Transformer V2. Accordingly, RegNetX-3.2gf is strongly recommended for practical applications. During the evaluation of Loss function and attention mechanism, the Cost-Sensitive Loss Function (CS) and the channel attention mechanism Squeeze-and-Excitation Networks (SE) demonstrated superior performance. The optimal model (RegNetX-3.2gf + CS + SE) achieved an average precision of 68.183%, an average recall of 63.722%, and an average F1 Score of 65.155%. This model exhibited significantly improved performance compared to the original dataset results, achieving an enhancement of 17.183% in precision and 10.655% in the F1 Score. Finally, the class activation maps demonstrate that our model focused on the cells themselves, especially on the nucleus when making classifications. It proved that our model was reliable. This study provided an important reference for the study of bone marrow cell classification and a practical application of the model, promoting the development of the intelligent classification of AML.