标题： 基于高效网络的迁移学习用于准确的白血病细胞分类 显示英文标题

标题： Transfer Learning with EfficientNet for Accurate Leukemia Cell Classification

摘要： 从外周血涂片图像中准确分类急性淋巴细胞白血病（ALL）对于早期诊断和有效的治疗计划至关重要。 本研究探讨了使用预训练卷积神经网络（CNNs）的迁移学习来提高诊断性能。 为了解决3,631张血液学图像和7,644张ALL图像数据集中的类别不平衡问题，我们应用了广泛的数据增强技术，创建了一个每类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。 EfficientNet-B3取得了最佳结果，F1分数为94.30%，准确率为92.02%，AUC为94.79%，在C-NMC挑战中优于之前报告的方法。 这些发现表明，结合数据增强与先进的迁移学习模型，特别是EfficientNet-B3，在开发用于血液系统恶性肿瘤检测的准确且稳健的诊断工具方面是有效的。 显示英文摘要

摘要： 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.