Abstract
Reactive lymphocytes may indicate diseases such as viral infections. Identifying these abnormal lymphocytes is crucial for disease diagnosis. Currently, reactive lymphocytes are mainly manually identified by pathological experts with microscopes and morphological knowledge, which is time-consuming and laborious. Some studies have used convolutional neural networks (CNNs) to identify peripheral blood leukocytes, but there are limitations in the small receptive field of the model. Our model introduces a transformer based on CNN, expands the receptive field of the model, and enables it to extract global features more efficiently. We also enhance the generalization ability of the model through virtual adversarial training (VAT) without changing the parameters of the model. Finally, our model achieves an overall accuracy of 93.66% on the test set, and the accuracy of reactive lymphocytes also reaches 88.03%. This work takes another step toward the efficient identification of reactive lymphocytes.