# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 2 Related Work
### Glaucoma Diagnosis Using Fundus Imaging
Each model was trained individually, and ultimately, XceptionNet, which offered the best performance  
Figure 1: Retina fundus image (on the **left**). In the provided images of the ROI for normal and glaucoma cases (on the **right**), the vertical dimension of the optic cup is referred to as the vertical cup diameter (VCD), and the vertical dimension of the optic disc is called the vertical disc diameter (VDD). These measurements are crucial for calculating the CDR, which is a significant parameter used in diagnosing glaucoma. The accurate measurement of VCD and VDD is essential for assessing the structural changes in the optic nerve head that are indicative of glaucoma. relative to the number of parameters, was chosen for classification experiments across multiple datasets, including HRF, DRISHITI-GS1, RIM-ONE, sjchoi86-HRF, and ACRIMA. The accuracies recorded were 80.0%, 75.2%, 71.2%, 70.8%, and 70.2%, respectively. [PERSON] et al. [13] utilized a deformable shape model, traditionally used for object detection, to segment the optic disc area into nine parts according to the ISNT rule, using the ORIGA dataset to achieve an AUC of 0.8384. [PERSON] et al. [14] used FFT coefficients and SVM to achieve an AUC of 0.87 from a personal dataset of 575 images, and [PERSON] et al. [15] used HOS, TT, DWT, and SVM to achieve an accuracy of 0.91 from a personal dataset of 60 images. [PERSON] et al. [16] used a 23-layer CNN combined with SVM to achieve an accuracy of 0.88 on the RIM-ONE dataset. [PERSON] et al. [17] used transfer learning with ResNet, Vgg16, and Inception v3 to achieve an AUC of 0.91 from a personal dataset of 14,822 images. [PERSON]- et al. [18] developed a model that automatically classifies color fundus images using a CNN and transfer learning processes, achieving an accuracy of 0.94 across three datasets, including two public (RIM-ONE and DRISHITI-GS1) and one private dataset comprising 2313 images. [PERSON] and [PERSON] et al. [19] devised a glaucoma detection model by employing two distinct methods and leveraging datasets, including RIM-ONE, ORIGA, and DRISHITI-GS. The segmentation of the boundary was facilitated using the 2D Fourier-Bessel series expansion-based empirical wavelet transform. Two approaches were explored: one based on machine learning (ML) models and the other utilizing an ensemble approach with the CNN architecture ResNet. The first method, executed at full scale, yielded the most favorable outcomes. For the second method, by employing the ensemble technique at full scale, the model achieved impressive performance metrics: 91.1% accuracy, 91.1% sensitivity, 94.3% specificity, 83.3% area AUC, and 96% receiver operating characteristic (ROC). Many existing studies demonstrate high AUC values, as well as high sensitivity and specificity. However, a closer look reveals that these results are often limited to private datasets, and many studies do not disclose the number of normal and abnormal data used for classification, resulting in a lack of confusion matrices. Even when confusion matrices are present, some papers achieve high AUCs primarily by correctly predicting normal data. While correctly identifying healthy individuals is important, the critical challenge is accurately diagnosing conditions such as glaucoma. The worst-case scenario is when a patient with glaucoma is misclassified as normal, remaining unaware of their condition and potentially worsening their situation. Unfortunately, many papers do not focus on or discuss this aspect sufficiently. we developed a model with a focus on improving the classification accuracy of glaucoma data while maintaining high accuracy for the primary screening in the health checkup process.