用EfficientNet提升白血病细胞分类准确率
Transfer Learning with EfficientNet for Accurate Leukemia Cell Classification
- 基于预训练模型做迁移学习，结合数据增强解决样本不平衡
- EfficientNet-B3达94.3%的F1分数，优于以往方法
- 适合医学图像分析与精准诊断研究者参考
从外周血涂片图像中准确分类急性淋巴细胞白血病（ALL）对早期诊断和治疗规划至关重要。本研究探讨了使用预训练卷积神经网络（CNN）进行迁移学习以提升诊断性能。针对包含3,631张血液学图像和7,644张ALL图像的数据集存在的类别不平衡问题，我们采用大量数据增强技术，构建了每类10,000张图像的平衡训练集。评估了ResNet50、ResNet101以及EfficientNet系列B0、B1、B3等模型。其中EfficientNet-B3表现最佳，F1分数达94.30%，准确率为92.02%，AUC为94.79%，优于此前在C-NMC挑战赛中报告的方法。结果表明，结合数据增强与先进迁移学习模型，特别是EfficientNet-B3，可有效构建精准且鲁棒的血液系统恶性肿瘤检测工具。
原文摘要 · Abstract (English)
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.
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