Table 1.
Review of studies using CNN method to assess retinal image quality.
| Author | Year | Database | Method (architecture) | Category of image quality | Definition of classification | Performance |
|---|---|---|---|---|---|---|
| Mahapatra et al.18 | 2016 | A DR screening initiative | CNN, and local saliency map | Gradable, ungradable | NA | Se: 98.2%, Sp: 97.8%, Acc: 97.9% |
| Yu et al.19 | 2017 | Kaggle | CNN(Alexnet), and saliency map | Good, poor | NA | Se: 96.63%, Sp: 93.10%, Acc: 95.42% |
| Saha et al.20 | 2017 | EyePACS | CNN (Alexnet) | Accept, reject, ambiguous | Yes | Acc: 100% |
| Zago et al.21 | 2018 | DRIMDB and ELSA-Brasil | CNN (Inception-v3) | Good, poor, outlier | NA |
DRIMDB: Se: 97.10%, Sp:100.0%, AUC: 99.98% ELSA-Brasil: Se: 92.00%, Sp: 96.00%,AUC: 98.56% |
| Chalakkala et al.22 | 2019 | DRIMDB, DR1–DR2, HRF, MESSIDOR, UoA-DR, Kaggle, IDRiD, | Six pre-trained CNN (AlexNet, GoogLeNet, ResNet50, ResNet101, Inception-v3, SqueezeNet) | MSRI high quality, low quality | Some databases | Se: 98.38%; Sp: 95.19%; Acc: 97.47% |
| Shen et al.23 | 2020 | Shanghai Diabetic Retinopathy Screening Program | Multi-task deep learning framework (VGG16) | Gradable, ungradable | Yes | Se: 83.62%, Sp: 91.72%, AUC: 94.55% |
| Yuen et al.24 | 2021 |
Primary dataset: CUHK Eye Center, National University Hospital External dataset: Hong Kong Children Eye Study, Queen’s University Belfast |
Two CNN (EfficientNet-B0, MobileNetV2) | Gradable, ungradable | Yes | Se: 92.1%, Sp: 98.3%, Acc: 92.5%, AUC: 97.5% |
DL deep learning, CNN convolutional neural network, MSRI medically suitable retinal image, NA not available, Se sensitivity, Sp specificity, Acc accuracy, AUC area under the curve.