Identification and visualization of fusion gene subtypes in APL using spatial attention mechanisms in vision models

Introduction Acute promyelocytic leukemia (APL) features leukemic cell differentiation arrest at the promyelocytic stage, mainly due to the t (15;17), (q24; q21) translocation that forms the PML-RARA fusion protein. Variant RARα translocations, with distinct biological traits and all-trans retinoic acid (ATRA) responses, often cause misdiagnosis and lengthy genetic testing. Methods To solve these problems, we propose a spatial attention mechanism-enhanced convolutional neural network integrating ResNet Blocks and a spatial attention module (CNN with spatial attention), which can achieve high-precision identification of APL fusion gene subtypes and pixel-level visualization of key areas. Data collected from two hospitals and Kaggle, including bone marrow smear images of PML-RARA, TTMV-RARA, NPM1-RARA, STAT5B-RARA, and NUP98-RARG subtypes, were preprocessed to form a five-class dataset. Results The model achieves an overall accuracy of 98.04% in five - class classification, with good performance in each category. The attention maps also enhance the model’s interpretability. Discussion Such a novel and rapid diagnostic approach for APL subtypes, which achieves high - precision identification and pixel - level visualization, holds significant clinical value.