Classification of Bone Marrow Cells Based on Dual-Channel Convolutional Block Attention Network

Abstract

Morphological differentiation between myeloblasts and monoblasts is pivotal for the majority of acute myeloid leukemia (AML) diagnosis in clinical settings. Manual morphology-based classification of blasts encounters challenges due to the limited differentiation of these bone marrow cells (BMC) of early stages. Hence, the utilization of artificial intelligence is essential to assist in the classification of these cells. 4001 single-cell images of monoblasts and myeloblasts were collected from Taizhou Hospital to form the BMC-1 dataset. The main novelties and features of the proposed method are as follows: 1) A maximum connected domain extraction method grounded in the watershed algorithm is introduced to efficiently eliminate stained impurity cells from single cell images. 2) A dynamic focal loss is introduced to gradually focus on difficult-to-classify samples as the training progresses. 3) A dual-channel convolutional block attention network (DCCBANet) is introduced to enhance feature extraction. It employs attention mechanisms to focus on key features, utilizing ordinary convolution for local feature extraction, dilated convolution for global feature extraction, and a decreasing dilation rate sequence to preserve detailed information. A macro F1-score (macro_F) of 96.8% is achieved on the BMC-1 validation dataset. Additionally, the presence of multiple differentiation types of granulocytes pose difficulties in granulocytes distinction. To further validate the efficacy of our proposed method on multi-classification tasks, we collected 6626 granulocyte single-cell images from Taizhou Hospital. Augmenting the dataset with 2441 granulocyte single-cell images from the public dataset BM_cytomorphology addressed sample shortages, forming the BMC-2 dataset. We applied our model to the BMC-2 dataset for experiments and ultimately achieved a macro_F of 87.49%. Our proposed method effectively distinguishes monoblasts and myeloblasts and excels in the classification of granulocytes.

Keywords

• Acute myeloid leukemia
• bone marrow cell classification
• convolutional neural network
• loss function
• attention mechanism