The results showed that the FSNet had high recognition and classification ability for HE images with different staining intensities of MX2 in the test set (Fig. 7e, f), and the area under the curve (AUC) values of the receiver operating characteristic (ROC) curve were 0.921 9, 0.876 2, 0.920 6 and 0.906 3 (negative, weak, strong and macro-images, respectively) (Fig. 7g, h), indicating that the model had a high predictive ability of MX2 expression level for HE images. Here, MX2 is linked to hereditary elliptocytosis.