Abstract: Most white blood cells (WBCs) recognition models present the disadvantages such as limited deployment due to large parameter count and computation amount, low WBC recognition accuracy, and poor generalization ability. Therefore, a lightweight and efficient WBCs recognition model based on improved EfficientNet is proposed. Firstly, the main modules are streamlined to reduce the model parameter count, while jump connections between feature layers are added to ensure a complete information flow. Secondly, the main module is optimized by adding the improved efficient channel attention and selecting a more suitable DropBlock2D. The improved module makes the model capture more channels and detail features, thus improving the recognition accuracy and generalization ability. Finally, the model is trained by a cross-entropy loss function with label smoothing to accelerate the convergence of the model and further enhance the generalization ability further. The experimental results show that the number of parameters of the improved model is 2.49M, which is 1.11M less than that before the improvement, simplifying the complexity of the model. The improved model achieves 99.67% accuracy in the classification task for the mixed dataset, which is 0.37% better than before the improvement. In addition, the model achieves 100.00% accuracy in the classification of the public dataset BCCD2, which is higher than the existing WBCs recognition models, verifying that the model has high accuracy and good generalization ability while maintaining lightweight computation.

Key words: WBC recognition, EfficientNet, channel attention, DropBlock2D