# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
###### Abstract  
This paper focuses on improving automated approaches to glaucoma diagnosis, a severe disease that leads to gradually narrowing vision and potentially blindness due to optic nerve damage occurring without the patient's awareness. Early diagnosis is crucial. By utilizing advanced deep learning technologies and robust image processing capabilities, this study employed four types of input data (retina fundus image, region of interest (ROI), vascular region of interest (VROI), and color palette images) to reflect structural issues. We addressed the issue of data imbalance with a modified loss function and proposed an ensemble model based on the vision large language model (VLLM), which improved the accuracy of glaucoma classification. The results showed that the models developed for each dataset achieved 1% to 10% higher accuracy and 8% to 29% improved sensitivity compared to conventional single-image analysis. On the REFUGE dataset, we achieved a high accuracy of 0.9875 and a sensitivity of 0.9. Particularly in the ORIGA dataset, which is challenging in terms of achieving high accuracy, we confirmed a significant increase, with an 11% improvement in accuracy and a 29% increase in sensitivity. This research can significantly contribute to the early detection and management of glaucoma, indicating potential clinical applications. These advancements will not only further the development of glaucoma diagnostic technologies but also play a vital role in improving patients' quality of life.  
glaucoma classification; computer aided diagnosis; deep learning; medical image analysis; data imbalance; VROI; ROI; color palette; vision large language model +
Footnote †: journal: Medical Image Analysis  
diagnose glaucoma. It is highly accessible and can be efficiently used in various clinical settings to provide large-scale examinations [2].  
Developing an accurate deep learning-based diagnostic system using retina fundus imaging for early glaucoma detection is essential. For precise diagnosis, it is necessary to incorporate a specialist's diagnostic perspective and identify the structural features of the fundus image. This paper explores the principles of glaucoma testing using retina fundus imaging, recent research outcomes, and how our proposed methods can contribute to glaucoma diagnosis. We also analyze the advantages and limitations compared to previous research methods and explore the potential of these methods as future diagnostic tools [3, 4]. This approach is crucial for enhancing the accuracy of glaucoma diagnosis, ensuring that more patients receive timely and appropriate treatment.  
The contributions of this study are as follows:  
1. _The proposal of a novel glaucoma diagnosis method utilizing optic nerve cells._ Previous research has utilized retina fundus or ROI images but has not provided enough features. This new research method additionally incorporates the surrounding vasculature and the structural morphology of the optic nerve cells. As it accounts for the structural features of glaucoma, this approach holds significant importance.
2. _Addressing the data imbalance issue in medical data by using a modified loss function._ It is common to use data augmentation or mix multiple datasets to address data imbalance issues in medical data. However, we have mitigated this problem by modifying the loss function. This approach is highly effective when the classification accuracy of certain classes is low due to data imbalance.
3. _A new ensemble model utilizing the vision large language model (VLLM)._ Traditional ensemble models typically employ methods such as voting, bagging, and boosting. The method we propose uses the predicted probabilities from four types of input data and their similarity to the actual answers to determine weights for each input data type for the ensemble. By reflecting the unique characteristics most similar to the correct answers among the input data types, this technique is a new ensemble approach that can enhance the final accuracy.