A Hybrid Swin Transformer and EfficientNetB2-Based Framework for Automated ROP Detection in Premature Infants

IF 2.5 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Imaging Systems and Technology Pub Date : 2025-08-29 DOI:10.1002/ima.70191

Ruoyao Cai, Jiaxuan Li, Fangzhou Wang, Xiaoya Li, Jiaming Guo, Jie Dai, Yingshan Shen

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引用次数: 0

Abstract

Retinopathy of prematurity (ROP) is the leading cause of childhood blindness, and early and accurate detection is crucial for timely intervention and vision protection. To address the challenges of subtle lesion features and class imbalance in ROP images, this study proposes a hybrid deep learning model that integrates a generative-discriminative collaborative mechanism and a multi-module feature fusion strategy, which combines the local detail extraction of EfficientNet-B2 with the global context modeling of swin transformer to enhance the robustness of fine-grained lesion perception and classification. Experimental evaluation on the dataset shows that our model achieves 96.71% accuracy and 97.65% specificity, significantly outperforming mainstream baseline models. The performance improvement has been statistically validated (p < 0.05). These results highlight the effectiveness of the model in addressing the challenges of ROP classification, providing a promising solution for intelligent assisted diagnosis, facilitating early disease warning, and promoting the application of artificial intelligence in ophthalmology.

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基于Swin变压器和efficientnetb2的早产儿ROP自动检测框架

早产儿视网膜病变（ROP）是儿童失明的主要原因，早期准确发现对于及时干预和保护视力至关重要。为了解决ROP图像中细微病变特征和类别不平衡的问题，本研究提出了一种融合生成-判别协同机制和多模块特征融合策略的混合深度学习模型，将EfficientNet-B2的局部细节提取与swin transformer的全局上下文建模相结合，增强了细粒度病变感知和分类的鲁棒性。对数据集的实验评估表明，我们的模型准确率为96.71%，特异性为97.65%，显著优于主流基线模型。性能改善经统计学验证（p < 0.05）。这些结果突出了该模型在解决ROP分类挑战方面的有效性，为智能辅助诊断、促进疾病早期预警、促进人工智能在眼科中的应用提供了有前景的解决方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术

CiteScore

6.90

自引率

6.10%

发文量

138

审稿时长

3 months

期刊介绍： The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.