Abstract
Accurate classification of Acute Lymphoblastic Leukemia (ALL) from peripheral blood smear images is essential for early diagnosis and effective treatment planning. This study investigates the use of transfer learning with pretrained convolutional neural networks (CNNs) to improve diagnostic performance. To address the class imbalance in the dataset of 3,631 Hematologic and 7,644 ALL images, we applied extensive data augmentation techniques to create a balanced training set of 10,000 images per class. We evaluated several models, including ResNet50, ResNet101, and EfficientNet variants B0, B1, and B3. EfficientNet-B3 achieved the best results, with an F1-score of 94.30%, accuracy of 92.02%, andAUCof94.79%,outperformingpreviouslyreported methods in the C-NMCChallenge. Thesefindings demonstrate the effectiveness of combining data augmentation with advanced transfer learning models, particularly EfficientNet-B3, in developing accurate and robust diagnostic tools for hematologic malignancy detection.
Abstract (translated)
急性淋巴细胞白血病（ALL）的准确分类对于早期诊断和有效治疗计划至关重要。本研究探讨了使用预训练卷积神经网络（CNN）进行迁移学习以提高诊断性能的方法。为了应对包含3,631张血液学图像和7,644张ALL图像的数据集中类别不平衡的问题，我们应用了大量的数据增强技术，创建了一个每类包含10,000张图像的平衡训练集。我们评估了几种模型，包括ResNet50、ResNet101以及EfficientNet变体B0、B1和B3。其中，EfficientNet-B3表现最佳，其F1得分为94.30%，准确率为92.02%，AUC为94.79%，优于C-NMC挑战赛中之前报道的方法。这些发现证明了结合数据增强与高级迁移学习模型（尤其是EfficientNet-B3）在开发准确且鲁棒的血液恶性肿瘤检测工具方面的有效性。
URL
https://arxiv.org/abs/2508.06535
