# Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysis

arXiv: https://arxiv.org/abs/2410.24046 · HTML: https://mlsanity.com/paper/2410.24046/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs](https://mlsanity.com/paper/1910.03667/) (2019)
- [Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning](https://mlsanity.com/paper/2407.16715/) (2024)
- [Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Function](https://mlsanity.com/paper/2408.11839/) (2024)
- [Breast Cancer Image Classification Method Based on Deep Transfer Learning](https://mlsanity.com/paper/2404.09226/) (2024)
- [Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits](https://mlsanity.com/paper/2408.07084/) (2024)
- [Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment](https://mlsanity.com/paper/2409.18544/) (2024)
- [Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation](https://mlsanity.com/paper/2410.13099/) (2024)
- [FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading](https://mlsanity.com/paper/2205.01676/) (2022)
- [Enhancing Convolutional Neural Networks with Higher-Order Numerical Difference Methods](https://mlsanity.com/paper/2409.04977/) (2024)
- [Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications](https://mlsanity.com/paper/2410.08553/) (2024)
- [Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature](https://mlsanity.com/paper/2409.14000/) (2024)

## Built on by (most cited first, 7 of 7)

- [Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning](https://mlsanity.com/paper/2411.06326/) (2024)
- [Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training](https://mlsanity.com/paper/2412.04718/) (2024)
- [Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification](https://mlsanity.com/paper/2411.12151/) (2024)
- [A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation](https://mlsanity.com/paper/2411.12157/) (2024)
- [AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP](https://mlsanity.com/paper/2501.14745/) (2024)
- [Optimizing Multi-Task Learning for Enhanced Performance in Large Language Models](https://mlsanity.com/paper/2412.06249/) (2024)
- [Balanced Soft mixture-of-expert model for Glaucoma Detection](https://mlsanity.com/paper/2607.25324/) (2026)

## Abstract (arXiv)

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.