Table 5. Exudates detection approaches.
| Year | Reference | Methods | Database | Results with evaluation metrics |
|---|---|---|---|---|
| 2024 | Van Do et al. (63) | Two-stage approach with CNN, VGGUnet, and RITM model | DDR, IDRiD | Mean Dice: 60.8%, 76.6%; mean IoU scores: 45.5%, 62.4% |
| 2024 | Guo et al. (64) | Deep multi-scale model, FCN for lesion segmentation | E-Ophtha, DDR, DIARETDB1, IDRiD | DSC: 69.31%; AUPR: 68.84% |
| 2024 | Maiti et al. (65) | Enriched encoder-decoder model with CLSTM and RES unit | DIARETDB0, Messidor, DIARETDB1, IDRiD | Overall accuracy: 97.7%; while accuracies of individual dataset respectively: 97.81%, 98.01%, 98.23%, 96.76% |
| 2023 | Farahat et al. (66) | U-Net and YOLOv5, Leaky ReLU | Cheikh Zaïd Foundation’s Ophthalmic Center | Specificity: 85%; sensitivity: 85%; accuracy: 99% |
| 2024 | Naik et al. (67) | DenseNet121 | APTOS-2019 | Accuracy: 96.64% |
| 2023 | Tang et al. (68) | Patch-wise density loss, global segmentation loss, discriminative edge inspection | IDRiD | Precision: 84.03%; recall: 65.14%; F1-score: 68.54 |
| 2022 | Reddy and Gurrala (69) |
Hybrid DLCNN-MGWO-VW, DSAM, and DDAM | IDRiD | Accuracy: 96.0% |
| 2022 | Hussain et al. (70) | YOLOv5M, classification-extraction-superimposition mechanism | EyePACS | Accuracy: 100% |
| 2023 | Sangeethaa and Jothimani (71) |
Median filtering, CLAHE | Local dataset: Aravind Eye Hospital, Coimbatore | Accuracy: 95% |
| 2021 | Sudha and Ganeshbabu (72) | Deep neural network, VGG-19, gradient descent, structure tensor | Kaggle | Sensitivity: 82%; accuracy: 96% |
| 2021 | Cincan et al. (73) | GoogLeNet, SqueezeNet, and ResNet50 models | SUSTech_SYSU | Accuracy: 0.928% |
| 2021 | Kurilová et al. (74) | Faster R-CNN, ResNet-50, region proposed network | DIARETDB1, Messidor, E-Ophtha-EX | AUC: 97.27%, 88.5%, 100% |
| 2020 | Pan et al. (75) | ResNet50, VGG-16, DenseNet, multi-label classification | 2nd Affiliated Hospital, Eye Center at Zhejiang University Hospital | AUC: 96.53% |
| 2020 | Theera-Umpon et al. (76) | Adaptive histogram equalization, hierarchical ANFIS, multilayer perceptron | DIARETDB1 | AUC: 99.8% |
| 2020 | Thulkar et al. (77) | Tree-based forward search approach, SVM | DIARETDB1, IDRiD, IDEDD | Specificity: 97.1%; sensitivity: 100% |
| 2019 | Khojasteh et al. (58) | DRBM, ResNet-50, CNNs | E-Ophtha, DIARETDB1 | Sensitivity: 99%; accuracy: 98% |
| 2019 | Chowdhury et al. (38) | Random forest and Naïve Bayes classifier approach | DIARETDB1, DIARETDB0 | Accuracy: 93.58% |
| 2018 | Lamet al. (37) | CNNs, AlexNet, GoogLeNet, VGG16, Inception-V3, ResNet | E-Ophtha | AUC: 95% |
| 2017 | Gondal et al. (54) | CNNs, for generate the class activation mappings | DIARETDB1 | AUC: 95.4% |
| 2016 | Paing et al. (55) | Artificial neural networks, and ROI iocalization | DIARETDB1 | Accuracy: 96% |
| 2015 | Prentašić and Lončarić (78) | Deep CNNs | DIARETDB1 | PPV: 77%; sensitivity: 77%; F1-score: 77% |
ANFIS, adaptive network-based fuzzy inference system; APTOS, Asia Pacific Tele-Ophthalmology Society; AUC, area under the curve; AUPR, area under the precision-recall curve; CLAHE, contrast-limited adaptive histogram equalization; CLSTM, contextual long-short term memory; CNN, convolutional neural network; DDAM, disease-dependent attention module; DLCNN, deep learning convolutional neural network; DRBM, discriminative restricted Boltzmann machine; DSC, dice similarity coefficient; DSAM, disease-specific attention module; DDR, dataset for diabetic retinopathy; DIARETDB, diabetic retinopathy database; E-Ophtha, ophthalmology; EX, exudates; EyePACS, eye picture archive communication system; FCN, fully convolutional network; IDEDD, Indian diabetic eye diseases dataset; IDRiD, Indian diabetic retinopathy image dataset; IoU, intersection over union; Messidor, methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology; MGWO, modified grey-wolf optimizer; PPV, positive predictive value; R-CNN, region-based convolutional neural network; ReLU, rectified linear unit; RES, residual extended skip; RITM, reviving iterative training with mask guidance; ROI, region of interest; SUS, Southern University of Science and Technology; SVM, support vector machine; SYSU, Sun Yat-sen university; VGG, visual geometry group; VW, variable weight; YOLOv5, you only look once version 5; YOLOv5M, you only look once version 5 medium.