# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 4 Experiment Implementation
### Datasets  
REFUGE [33] (retina fundus glaucoma challenge) is a dataset provided to the Grand Challenge hosted by the Medical Image Computing and Computer Assisted Intervention Institute in Spain. The contests are given in the glaucoma classification, the segmentation of the optic disc and cup, and the segmentation of the macula. A total of 1200 sheets of data, with 400 sheets each for training, validation, and testing, are composed of 360 normal  
Figure 6: EfficientNet V2 structure (on the **left**); Fused-MBConv and MBConv (on the **right**). MBConv: mobile vision convolutional network; SE: squeeze and excitation; FC: full connect; H: height; W: width; C: number of channels.  
sheets and 40 glaucoma sheets. It has a JPG format, and the training data have a resolution of 2124 \(\times\) 2056, and the validation and test data have a resolution of 1634 \(\times\) 1634.  
ORIGA [34] (an online retina fundus image database for glaucoma analysis and research) aims to provide clinical ground truth to benchmark segmentation and classification algorithms. It uses a custom-developed tool to generate manual segmentation for OD and OC. It also provides CDR and labels for each image as glaucomatous or healthy. This dataset has been used as a standard dataset in some of the recent state-of-the-art research for glaucoma classification. The dataset was collected by the Singapore Eye Research Institute and has 482 healthy images and 168 glaucomatous images.  
The G1020 [35] dataset is a significant collection that is specifically designed to aid in the computer-aided detection and classification of glaucoma through retina fundus images. It includes 1020 high-resolution color fundus images collected under standard ophthalmological practices to provide a robust benchmark for glaucoma diagnosis. These images are annotated with critical details such as the vertical cup-to-disc ratio, the size of the neuroretinal rim across different quadrants, and the locations of the optic disc and optic cup. The dataset is composed of JPG images with a resolution of 3004 \(\times\) 2423. It includes 580 images of normal data and 237 images of glaucoma data.  
AI-HUB: This is a dataset built between 2018 and 2019 for Koreans led by Konyang University. It consists of wide-angle fundus images for macular degeneration and diabetic retinopathy and general fundus images for glaucoma. Three residents performed reading and inspection work to classify the data, and after two specialists performed reading and total inspection, only data with a 100% reading agreement among specialists were constructed as the final dataset. The general fundus image consisted of a total of 3372 images, including 1806 for glaucoma and 1566 for normal. Glaucoma has a resolution of 2796 \(\times\) 2848, and normal has a resolution of 1964 \(\times\) 2000. It was taken using Canon CR-2 equipment and is in jpg format.  
As an ophthalmology hospital located in Daejeon, Korea, this is a private hospital that specializes in the treatment of glaucoma and cataract. Fundus images collected from Korean subjects consisted of a total of 583 images, including 299 normal and 284 glaucoma images. It has a resolution of 1270 \(\times\) 793 and was used after converting to jpg format.  
For datasets provided with separate training and testing sets, the data were split into a training-validation ratio of 8:2, forming the training and validation sets. For datasets where the training and testing sets were not provided separately, a ratio of 7:2:1 was used to split the data into training, validation, and testing sets. The training and validation sets were shuffled using the k-fold method. Table 1 summarizes the datasets we used. This study aimed for customized analysis tailored to the unique characteristics of each dataset, given that the imaging equipment used varies across datasets. Therefore, we did not mix it with other datasets.