Deep Learning-Based Image-Level Detection of APL-Related Azurophilic Granule-PositiveLeukemia Cells
Lin Zhiyuan1, Liu Xin2, Qiu Jinming3, Mai Xiuqu4, Zhang Fuhua4, Xu Jiayu4*
1(School of Mechatronic Engineering and Automation, Foshan University, Foshan 528000,Guangdong, China) 2(School of Design, Foshan University, Foshan 528000, Guangdong, China) 3(Department of Radiology, The Sixth Affiliated Hospital of South China University of Technology, Foshan 528000, Guangdong, China) 4(Department of Hematology and Lymphoma, The Sixth Affiliated Hospital of South China University of Technology, Foshan 528000, Guangdong, China)
Abstract：The aim of this work is to develop a deep learning method for automatically identifying acute promyelocytic leukemia (APL)-related azurophilic granule-positive microscopic images, to improve the efficiency of morphological prescreening and support the early auxiliary diagnosis of APL in emergency clinical settings. A total of 450 leukocyte microscopic images collected from Nanhai People′s Hospital were used to construct an image-level binary classification dataset for the presence or absence of typical azurophilic granules, including 230 negative and 220 positive images. The dataset was divided into training and test sets at a ratio of 8∶2 using stratified random sampling. An end-to-end network, termed EADF-Net, was proposed based on EfficientNet-B4, which incorporated a feature pyramid attention module to capture multi-scale contextual information, a multi-scale feature fusion module to enhance fine-grained granule and boundary representations, and a context enhancement module to aggregate local and global contextual features. During training, binary cross-entropy loss, a Sobel gradient-based boundary enhancement regularization term, and supervised contrastive loss were jointly optimized. The proposed model was compared with ResNet-50, DenseNet-121, EfficientNet variants, and Swin Transformer under the same training settings. On the test set, EADF-Net achieved an AUC of 92.2%, accuracy of 90.0%, sensitivity of 84.1%, specificity of 95.7%, and F1-score of 89.1%. Compared with ResNet-50, EfficientNet-B4, EfficientNetV2-S, and Swin Transformer, EADF-Net showed better overall performance while maintaining a smaller model size. Ablation experiments further demonstrated that the combination of FPA, MSFF, and CEM effectively improved the model′s ability to discriminate fine-grained morphological features of APL. In conclusion, EADF-Net enabled stable discrimination of APL-related azurophilic granule-positive microscopic images under limited-sample conditions, while achieving improved performance with controlled model complexity, suggesting its potential as an auxiliary tool for early APL screening.
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