# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 2 Related Work  
### Glaucoma Diagnosis Using Fundus Imaging  
A wide range of techniques, from basic machine learning methods to advanced deep learning approaches, have been proposed for detecting glaucoma. Typically, studies using machine learning techniques have adopted the methods used by ophthalmologists to examine retina images. Initially, the optic disc area is segmented from the fundus image, and then glaucoma is classified based on features such as the optic cup-to-disc ratio (CDR) and the ISNT rule. This content is explained in Figure 1.  
[PERSON] et al. [5] utilized the superpixel technique to segment the optic disc area and calculated the CDR using brightness differences. Experiments were conducted using the SiMES and SCES datasets, achieving AUC results of 83% and 88%, respectively. [PERSON] et al. [6] employed Hough transform to locate the optic disc area and extracted features using the projection texture and bag of words techniques. The classification between normal and glaucomatous cases was carried out using a support vector machine (SVM). By utilizing the DRISHIT-GS11 dataset, this study reported an accuracy of 76.8% and an AUC of 78.0%. [PERSON] et al. [7] enhanced contrast in fundus images by removing noise through preprocessing. He then used the linear iterative clustering superpixel technique to segment the optic disc area and determined glaucoma based on CDR values; a CDR of between 0.4 to 0.6 and glaucomatous when it exceeded 0.6 was considered normal. [PERSON] et al. [8] applied a 2D discrete wavelet transform to extract features and trained a neural network. By using the HRF dataset, an accuracy of 95.8% was achieved. [PERSON] et al. [9] also applied wavelet transform to extract features from fundus images, not directly from the original but from transformed images in red, green, blue, and grayscale. The features were classified using a least squares SVM. Testing with the RIM-ONE dataset yielded an accuracy of 81.3%. Research on detecting glaucoma using deep learning has been actively pursued alongside traditional machine learning methods. [PERSON] et al. [10] employed a U-Net model to extract the optic disc area, which was then transformed into polar co-ordinates to spread around the center for analysis. The classification model used was ResNet, and it was trained on the original images, the optic disc region images, and the modified images. Three models were trained on each input type, and their results were combined using a voting ensemble method. The experiments conducted with the SCES and SINDI datasets yielded accuracies of 83.2% and 66.6%, respectively. [PERSON] et al. [11] preprocessed original fundus images to generate six feature images, which were then fused to locate the central point of the optic nerve area. The six feature images included a bottom-hat transformation image, a top-hat transformation image, a combined bottom-top-hat transformation image, an enhanced brightness image, a blood vessel extraction image, and a composite image of the previous five features. Following the identification of the optic disc's central point, the optic disc and surrounding areas were segmented separately using U-Net. The segmented optic disc area was then subtracted from the surrounding area to form a donut-shaped region, which was segmented into quarters according to the ISNT rule and classified using a random forest method. By utilizing the ORIGA dataset, this study achieved an accuracy of 76.9% and an AUC of 83.1%. Although the separation of the optic disc and surrounding area was managed through deep learning, the process of locating the optic disc area and classifying normal from glaucoma leaned more towards machine learning techniques, complicating the classification of the approach as being purely based on deep learning. [PERSON] et al. [12] used five different CNN models (InceptionV3, XceptionNet, VGG16, VGG19, and ResNet50) to classify normal and glaucomatous cases.