This study introduces the Hybrid Multi-modal VGG (HM-VGG) model, a cutting-edge deep learning approach for the early diagnosis of glaucoma. The HM-VGG model utilizes an attention mechanism to process Visual Field (VF) data, enabling the extraction of key features that are vital for identifying early signs of glaucoma. Despite the common reliance on large annotated datasets, the HM-VGG model excels in scenarios with limited data, achieving remarkable results with small sample sizes. The model's performance is underscored by its high metrics in Precision, Accuracy, and F1-Score, indicating its potential for real-world application in glaucoma detection. The paper also discusses the challenges associated with ophthalmic image analysis, particularly the difficulty of obtaining large volumes of annotated data. It highlights the importance of moving beyond single-modality data, such as VF or Optical Coherence Tomography (OCT) images alone, to a multimodal approach that can provide a richer, more comprehensive dataset. This integration of different data types is shown to significantly enhance diagnostic accuracy. The HM- VGG model offers a promising tool for doctors, streamlining the diagnostic process and improving patient outcomes. Furthermore, its applicability extends to telemedicine and mobile healthcare, making diagnostic services more accessible. The research presented in this paper is a significant step forward in the field of medical image processing and has profound implications for clinical ophthalmology.

翻译：本研究介绍了混合多模态VGG（HM-VGG）模型，这是一种用于青光眼早期诊断的前沿深度学习方法。HM-VGG模型利用注意力机制处理视野（VF）数据，能够提取对识别青光眼早期迹象至关重要的关键特征。尽管通常依赖大规模标注数据集，但HM-VGG模型在数据有限的场景中表现卓越，即使在小样本量下也能取得显著成果。该模型在精确率、准确率和F1分数方面的高指标凸显了其性能，表明其在青光眼检测的实际应用中具有潜力。本文还讨论了与眼科图像分析相关的挑战，特别是获取大量标注数据的困难。研究强调，需要超越单一模态数据（如仅使用VF或光学相干断层扫描（OCT）图像），转向能够提供更丰富、更全面数据集的多模态方法。不同类型数据的整合被证明能显著提升诊断准确性。HM-VGG模型为医生提供了一种有前景的工具，可简化诊断流程并改善患者预后。此外，其适用性可延伸至远程医疗和移动医疗领域，使诊断服务更易于获取。本文提出的研究是医学图像处理领域的重要进展，对临床眼科学具有深远意义。