一种轻型双输出视觉转换器,用于增强CT图像对肺结节的分类。

IF 2.8 4区 医学 Q3 ONCOLOGY
Technology in Cancer Research & Treatment Pub Date : 2025-01-01 Epub Date: 2025-08-21 DOI:10.1177/15330338251370439
Menna Allah Mahmoud, Yanhua Wen, Yuling Liufu, Xiaohuan Pan, Ruihua Su, Yubao Guan
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引用次数: 0

摘要

本研究评估了具有双输出架构的轻型视觉转换器(EfficientFormerV2-S2)用于肺结节分类的有效性,评估了其在多个数据集上的性能和通用性。方法本研究使用了来自三个来源的数据集:Institution 1(936张图像)、Institution 2(280张图像)和公共Zenodo数据集(308张图像),包括腺癌、鳞状细胞癌和良性病变。模型评估包括保留验证、五倍交叉验证和针对PneumoniaMedMNIST数据集的基准测试。实现了综合图像预处理和增强技术。结果该模型在所有数据集上均表现出鲁棒性,对机构1、机构2和机构Zenodo的测试准确率分别为92.62±1.65%、97.14±1.78%和95.74±1.35%。交叉验证结果显示,在最小的可变性(标准偏差)下,性能一致
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Lightweight Dual-Output Vision Transformer for Enhanced Lung Nodule Classification Using CT Images.

A Lightweight Dual-Output Vision Transformer for Enhanced Lung Nodule Classification Using CT Images.

A Lightweight Dual-Output Vision Transformer for Enhanced Lung Nodule Classification Using CT Images.

A Lightweight Dual-Output Vision Transformer for Enhanced Lung Nodule Classification Using CT Images.

IntroductionThis study evaluates the effectiveness of a lightweight vision transformer (EfficientFormerV2-S2) with a dual-output architecture for lung nodule classification, assessing its performance and generalizability across multiple datasets.MethodsThe study utilized datasets from three sources: Institution 1 (936 images), Institution 2 (280 images), and a public Zenodo dataset (308 images), comprising adenocarcinoma, squamous cell carcinoma, and benign lesions. Model evaluation included holdout validation, five-fold cross-validation, and benchmarking against the PneumoniaMedMNIST dataset. Comprehensive image preprocessing and augmentation techniques were implemented.ResultsThe model demonstrated robust performance across all datasets, achieving test accuracies of 92.62 ± 1.65%, 97.14 ± 1.78%, and 95.74 ± 1.35% for Institutions 1, 2, and Zenodo respectively. Cross-validation results showed consistent performance with minimal variability (standard deviations <2%). On the PneumoniaMedMNIST benchmark, our optimized model achieved superior performance (accuracy: 0.936, AUC: 0.981) compared to ResNet18 and ResNet50 benchmarks.ConclusionThe lightweight transformer-based model demonstrates excellent performance and generalizability across multiple institutional datasets, suggesting its potential for efficient clinical implementation in lung nodule classification tasks.

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来源期刊
CiteScore
4.40
自引率
0.00%
发文量
202
审稿时长
2 months
期刊介绍: Technology in Cancer Research & Treatment (TCRT) is a JCR-ranked, broad-spectrum, open access, peer-reviewed publication whose aim is to provide researchers and clinicians with a platform to share and discuss developments in the prevention, diagnosis, treatment, and monitoring of cancer.
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