## 2 Novel Diabetic Retinopathy (DR) Detection Framework and Experiment
### Methodology
#### 2.2.1 Data Description  
A large number of data, namely optical images, were obtained from the Kaggle Blindness Challenge dataset APTOS 2019, ([[https://www.kaggle.com/competitions/aptos2019-blindness-detection/overview](https://www.kaggle.com/competitions/aptos2019-blindness-detection/overview)]([https://www.kaggle.com/competitions/aptos2019-blindness-detection/overview](https://www.kaggle.com/competitions/aptos2019-blindness-detection/overview))), accessed on 25 February 2025, which contained separate cases for testing and training. The training dataset contained a total of 3662 images, divided into different classes (0--Normal; 1--Mild; 2--Moderate; 3--Severe; and 4--Proliferative) by expert clinicians. The distribution of the images belonging to each class is reported in Table 2.  
A substantial number of images was captured at the Aravind Eye Hospital in India [31]. Numerous retinal images were captured using fundus photography under a variety of imaging conditions. Based on prior knowledge, expert clinicians assessed the severity of the DR in each image on a scale of 0 to 4. The fundus images of the eyes served as the default images. Fundus photography is the standard and most commonly used imaging technique used to capture detailed views of the interior surface of the eye, such as the retina, optic disc, macula, and blood vessels. This is due to several factors, such as (i) their diagnostic importance in detecting and monitoring conditions such as DR, glaucoma, macular degeneration, and hypertensive retinopathy; (ii) their common use in ophthalmology for routine eye scans and telemedicine, as well as in research; and (iii) their high resolution due to the use of a non-invasive method. Moreover, fundus images form the baseline for AI and research, as many models and medical databases use fundus images as primary references for the detection of eye diseases.  
In the current research, the previously verified images were considered as a priori information, because they were verified, diagnosed, and rated according to standard screening and diagnostic criteria. Specifically, the images were obtained via the digital slit lamp in the lab and had been clinically verified before processing. Moreover, the two groups of patients were verified in the lab by labeling their conditions as normal or abnormal (including severe conditions) to establish the ground truth.  
The ground truth is the most accurate and reliable label for a dataset. It is applied for the training, validation, and testing of frameworks. It ensures that the model learns the correct patterns and generates accurate predictions. The sources for the ground truth in DR diagnosis are the annotations of expert ophthalmologists, who manually grade the images according to standardized DR classification systems (e.g., the International Clinical DR (ICDR) scale). Specifically, multiple experts annotate the images, and consensus or majority voting is used to reduce the risk of bias and inter-observer variability. Other ground truth sources could be publicly available labeled datasets, such as APTOS, which  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Class** & **Number of Images** \\ \hline No DR & 1805 \\ \hline Mild DR & 370 \\ \hline Moderate DR & 999 \\ \hline Severe DR & 193 \\ \hline Proliferative DR & 295 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The APTOS 2019 dataset: multi-class image distribution.  
provides labeled DR images with ground truth annotations that are verified by experts. In the current work, both aforementioned sources were used for additional verification.