# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 2 Related Work
### Data Imbalance  
In the field of medical imaging, unlike general imagery, it is challenging to obtain data due to privacy concerns involving patient information and legal issues surrounding medical regulations. Furthermore, there's a significant imbalance in the data available, with a majority of the tested population typically being healthy and a scarcity of data on those with conditions.  
This imbalance between the types of data used is an inherent and ongoing issue. Particularly in medicine, where the majority of the tested population is healthy, and those with diseases are in the minority, medical fields are emblematic of sectors where data imbalance is prevalent. Numerous studies have been conducted to address this issue, using models ranging from machine learning to deep learning.  
Traditional methods include undersampling and oversampling techniques. Undersampling involves reducing the data from the majority class to match the quantity of the minority class data. This can range from random sampling, where data is randomly selected, to methods such as Tomek Links, which eliminate the majority class data that are nearest to the minority class. There is also the NN-rule method, which uses the K nearest neighbor (KNN) approach to create subsets of data by randomly selecting from the majority class to align with the entire minority class data. Another method, one-sided selection, combines Tomek links and NN-rules. While undersampling can quickly address data imbalance, it also reduces the amount of data available, leading to the potential loss of information.  
On the other hand, oversampling adjusts the amount of minority class data to equal that of the majority class. This can be carried out through simple resampling, which involves randomly duplicating data from the minority class, or through more advanced methods, such as SMOTE, which synthesizes new minority class data points by using existing data. Borderline SMOTE focuses on creating synthetic data along the border between two classes. Similarly, ADASYN [20] adapts the amount of data it generates based on the density of the majority class data surrounding each minority class data point. Traditional machine learning has expanded these concepts into variants such as SMOTENC [21], SVM-SMOTE [22], and KNN-SMOTE [23], which combine SMOTE with various algorithms.  
However, oversampling can lead to over-fitting due to the similarity between the newly generated data points, and it may perform poorly with noisy data or outliers, making it difficult to distinguish the boundaries between classes.  
Due to the limitation of not being able to use most of the normal data with undersampling, oversampling techniques were used sparingly. Given the issue of data homogeneity and the overall scarcity of data, oversampling was not extensively employed. When the numbers were too small to adequately split into training, validation, and testing sets, oversampling was applied between the training and validation phases. Subsequently, only data augmentation methods that did not alter the original data were used to increase the dataset size.