Table 2 Related works in diabetic retinopathy diagnosis part - 1.
From: Ensemble deep learning and EfficientNet for accurate diagnosis of diabetic retinopathy
S. no. | References | Methodologies | Data source | Advantages | Disadvantages and gaps |
|---|---|---|---|---|---|
1 | Gulshan et al.9 | CNN-based deep learning model for binary DR classification | Retinal fundus images from EyePACS dataset | Demonstrated high sensitivity and specificity in a clinical setting | Limited to binary classification (DR vs. no DR); lacks granularity in severity levels and interpretability |
2 | Ting et al.10 | CNNs trained on multiethnic retinal images, with system fine-tuned for DR severity prediction | Retinal images from multiethnic EyePACS, US and Singapore datasets | Enhanced generalizability across ethnicities and improved accuracy | High computational demand; limited explainability for clinical use |
3 | Abràmoff et al.11 | Autonomous AI model with convolutional neural networks for DR diagnosis | EyePACS data from primary care settings | High applicability in clinical settings with autonomous operation | Focuses on binary classification; does not provide insights into DR severity grading |
4 | Li et al.12 | Multi-stage CNN framework targeting vision-threatening DR | Large dataset of color fundus photographs | Specialized in detecting severe DR cases, improving triaging | Limited interpretability and generalization due to specialized target |
5 | Bellemo et al.13 | AI-based CNN model tailored for DR screening in low-resource settings | Retinal fundus images from African clinical settings | Validated model efficacy in diverse, resource-limited regions | Model scalability is constrained; limited interpretability for practical diagnostic insights |
6 | Bhaskaranand et al.14 | Deep learning-based automated screening and monitoring system for DR | Retinal fundus images | Offers continuous monitoring of DR progression, supports early detection | Limited interpretability in decision-making, lacking an advanced explanation-guided method |
7 | Sahlsten et al.15 | Developed and validated DL algorithms for DR on a large, diverse population | Multiethnic population data | High robustness due to diverse population data, ensuring generalizability | No focused methodology for interpretability, which can limit clinician trust |
8 | Raman et al.16 | Developed DL models on non-mydriatic images to detect DR | Electronic health records | Supports diagnosis using non-mydriatic images, allowing more accessible and frequent testing | Limited attention mechanisms, interpretability concerns |
9 | Takahashi et al.17 | Dual-purpose model assessing both DR and glaucoma | Retinal fundus photographs | Supports multi-disease diagnosis, enhancing model utility in broader ophthalmology | Lacks targeted grading for different DR severity stages, gaps in explaining results for individual conditions |
10 | Burlina et al.18 | Convolutional Neural Networks for automated grading of age-related macular degeneration (AMD) | Color fundus images | High accuracy in AMD grading, shows potential for adaptation to related conditions such as DR | Focus on AMD limits direct applicability to DR, interpretability and DR severity grading are not addressed |
