White blood cell classification network using MobileNetv2 with multiscale feature extraction module and attention mechanism

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White blood cells play a crucial role in the human immune system. The accurate classification of white blood cells can help doctors diagnose various diseases for patients. To enhance the classification accuracy of white blood cells micro-vision images, an efficient lightweight deep learning network called ICAFF-MobileNetv2 is proposed in this paper. Firstly, the pruning operations are applied to the MobileNetv2 and the multiscale feature extraction module is proposed to reduce the model size while ensuring the classification accuracy. Secondly, the improved coordinate attention mechanism module and the attention feature fusion module are incorporated into the inverted residual structures to enhance the feature extraction capability and the classification accuracy of the network. Furthermore, considering the imbalance among different types of white blood cells in the datasets, the method of label smoothing is introduced to improve the cross-entropy loss function and further enhance the classification accuracy. Finally, the improved network is validated on the proposed mixed dataset. The experimental results demonstrate that the proposed network features high classification accuracy and small model size. Compared to other classification networks, ICAFF-MobileNetv2 exhibits superior performance with 98.54 %, 98.21 %, 98.56 %, and 98.38 % of the classification accuracy, recall, precision and F1 score respectively, which are 0.7 %, 0.86 %, 0.92 %, and 0.89 % better than MobileNetv2.