Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey
Abstract
:1. Introduction
- No Apparent Retinopathy: No abnormalities.
- Mild Non-Proliferative Diabetic Retinopathy (NPDR): This is the first stage of diabetic retinopathy, specifically characterized by tiny areas of swelling in retinal blood vessels known as Microaneurysms (MA) [8]. There is an absence of profuse bleeding in retinal nerves and if DR is detected at this stage, it can help save the patient’s eyesight with proper medical treatment (Figure 1A).
- Moderate NPDR: When left unchecked, mild NPDR progresses to a moderate stage when there is blood leakage from the blocked retinal vessels. Additionally, at this stage, Hard Exudates (Ex) may exist (Figure 1B). Furthermore, the dilation and constriction of venules in the retina causes Venous Beadings (VB) which are visible ophthalmospically [8].
- Severe NPDR: A larger number of retinal blood vessels are blocked in this stage, causing over 20 Intra-retinal Hemorrhages (IHE; Figure 1C) in all 4 fundus quadrants or there are Intra-Retinal Microvascular Abnormalities (IRMA) which can be seen as bulges of thin vessels. IRMA appears as small and sharp-border red spots in at least one quadran. Furthermore, there can be a definite evidence of VB in over 2 quadrants [8].
- Proliferative Diabetic Retinopathy (PDR): This is an advanced stage of the disease that occurs when the condition is left unchecked for an extended period of time. New blood vessels form in the retina and the condition is termed Neovascularization (NV). These blood vessels are often fragile, with a consequent risk of fluid leakage and proliferation of fibrous tissue [8]. Different functional visual problems occur at PDR, such as blurriness, reduced field of vision, and even complete blindness in some cases (Figure 1D).
2. Methods
2.1. Literature Search Details
- PubMed: Publications from MEDLINE (https://pubmed.ncbi.nlm.nih.gov/ accessed on date 14 June 2021)
- IEEE Xplore: IEEE conference & journals (https://ieeexplore.ieee.org/Xplore/home.jsp accessed on 14 June 2021)
- PUBLONS: Publications from Web of Science (https://publons.com/about/home/ accessed on 14 June 2021)
- SPIE digital library: Conference & journals from SPIE (https://www.spiedigitallibrary.org/ accessed on 14 June 2021)
- Google Scholar: Conference and journal proceedings from multiple databases (https://scholar.google.co.in/ accessed on 14 June 2021).
- Using the predefined set of keywords and logical operators, a small set of papers were identified in this time range (2016–2021).
- Using a manual search strategy, the papers falling outside the scope of this review were eliminated.
- The duplicate articles (i.e., the papers occurring in multiple databases) were eliminated to obtain the set of unique articles.
2.2. Dataset Search Details
- 4.
- The original papers and websites associated with each dataset were analyzed and a systematic, tabular representation of all available information was created.
- 5.
- The Google dataset search and different forums were checked for missing dataset entries and step 2 was repeated for all original datasets found.
- 6.
- A final comprehensive list of datasets and its details was generated and represented in Table 1.
3. Results
3.1. Dataset Search Results
- Public open access (OA) datasets with high quality DR grades.
- DR datasets, that can be accessed upon request, i.e., can be accessed by filling necessary agreements and forms for fair usage; they are a sub-type of (OA) databases and are termed Access Upon Request (AUR) in the table.
- Private datasets from different institutions that are not publicly accessible or require explicit permission can access are termed Not Open Access (NOA).
3.2. Diabetic Retinopathy Classification
3.2.1. Machine Learning Approaches
3.2.2. Deep Learning Approaches
3.3. Diabetic Retinopathy Lesion Segmentation
3.3.1. Machine Learning and Un-Machine Learning Approaches
3.3.2. Deep Learning Approaches
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Dataset | No. of Image | Device Used | Access | Country | Year | No. of Subjects | Type | Format | Remarks |
---|---|---|---|---|---|---|---|---|---|
DRIVE [29] | 40 | Canon CR5 non-mydriatic 3CCD camera with a 45° FOV | OA | Netherlands | 2004 | 400 | Fundus | JPEG | Retinal vessel segmentation and ophthalmic diseases |
DIARETDB0 [30] | 130 | 50° FOV DFC | OA | Finland | 2006 | NR | Fundus | PNG | DR detection and grading |
DIARETDB1 [31] | 89 | 50° FOV DFC | OA | Finland | 2007 | NR | Fundus | PNG | DR detection and grading |
National Taiwan University Hospital [32] | 30 | Heidelberg retina tomography with Rostock corneal module | OA | Japan | 2007–2017 | 30 | Fundus | TIFF | DR, pseudo exfoliation |
HEI-MED [33] | 169 | Visucam PRO fundus camera (Zeiss, Germany) | OA | USA | 2010 | 910 | Fundus | JPEG | DR detection and grading |
19 CF [34] | 60 | NR | OA | Iran | 2012 | 60 | Fundus | JPEG | DR detection |
FFA Photographs & CF [35] | 120 | NR | OA | Iran | 2012 | 60 | FFA | JPEG | DR grading and lesion detection |
Fundus Images with Exudates [36] | 35 | NR | OA | Iran | 2012 | NR | Fundus | JPEG | Lesion detection |
DRiDB [37] | 50 | Zeiss VISUCAM 200 DFC at a 45° FOV | AUR | Croatia | 2013 | NR | Fundus | BMP files | DR grading |
eOphtha [38] | 463 | NR | OA | France | 2013 | NR | Fundus | JPEG | Lesion detection |
Longitudinal DR screening data [27] | 1120 | Topcon TRC-NW65 with a 45 degrees field of view | OA | Netherlands | 2013 | 70 | Fundus | JPEG | DR grading |
22 HRF [39] | 45 | CF-60UVi camera (Canon) | OA | Germany and Czech Republic | 2013 | 45 | Fundus | JPEG | DR detection |
RITE [40] | 40 | Canon CR5 non-mydriatic 3CCD camera with a 45° FOV | AUR | Netherlands | 2013 | Same As Drive | Fundus | TIFF | Retinal vessel segmentation and ophthalmic diseases |
DR1 [41] | 1077 | TRC-50× mydriatic camera Topcon | OA | Brazil | 2014 | NR | Fundus | TIFF | DR detection |
DR2 [41] | 520 | TRC-NW8 retinography (Topcon) with a D90 camera (Nikon, Japan) | OA | Brazil | 2014 | NR | Fundus | TIFF | DR detection |
DRIMDB [42] | 216 | CF-60UVi fundus camera (Canon) | OA | Turkey | 2014 | NR | Fundus | JPEG | DR detection and grading |
FFA Photographs [43] | 70 | NR | OA | Iran | 2014 | 70 | FFA | JPEG | DR grading and Lesion detection |
MESSIDOR 1 [44] | 1200 | Topcon TRC NW6 non-mydriatic retinography, 45° FOV | OA | France | 2014 | NR | Fundus | TIFF | DR and DME grading |
Lotus eyecare hospital [45] | 122 | Canon non-mydriatic Zeiss fundus camera 90° FOV | NOA | India | 2014 | NR | Fundus | JPEG | DR detection |
Srinivasan [46] | 3231 | SD-OCT (Heidelberg Engineering, Germany) | OA | USA | 2014 | 45 | OCT | TIFF | DR detection and grading, DME, AMD |
EyePACS [47] | 88,702 | Centervue DRS (Centervue, Italy), Optovue iCam (Optovue, USA), Canon CR1/DGi/CR2 (Canon), and Topcon NW (Topcon) | OA | USA | 2015 | NR | Fundus | JPEG | DR grading |
Rabbani [48] | 24 images & 24 videos | Heidelberg SPECTRALIS OCT HRA system | OA | USA | 2015 | 24 | OCT | TIFF | Diabetic Eye diseases |
DR HAGIS [49] | 39 | TRC-NW6s (Topcon), TRC-NW8 (Topcon), or CR-DGi fundus camera (Canon) | OA | UK | 2016 | 38 | Fundus | JPEG | DR, HT, AMD and Glaucoma |
JICHI DR [50] | 9939 | AFC-230 fundus camera (Nidek) | OA | Japan | 2017 | 2740 | Fundus | JPEG | DR grading |
Rotterdam Ophthalmic Data Repository DR [51] | 1120 | TRC-NW65 non-mydriatic DFC (Topcon) | OA | Netherlands | 2017 | 70 | Fundus | PNG | DR detection |
Singapore National DR Screening Program [52] | 494,661 | NR | NOA | Singapore | 2017 | 14,880 | Fundus | JPEG | DR, Glaucoma and AMD |
IDRID [53] | 516 | NR | OA | India | 2018 | NR | Fundus | JPEG | DR grading and lesion segmentation |
OCTID [54] | 500+ | Cirrus HD-OCT machine (Carl Zeiss Mediatec) | OA | Multi ethnic | 2018 | NR | OCT | JPEG | DR, HT, AMD |
UoA-DR [55] | 200 | Zeiss VISUCAM 500 Fundus Camera FOV 45° | AUR | India | 2018 | NR | Fundus | JPEG | DR grading |
APTOS [56] | 5590 | DFC | OA | India | 2019 | NR | Fundus | PNG | DR grading |
CSME [57] | 1445 | NIDEK non-mydriatic AFC-330 auto-fundus camera | NOA | Pakistan | 2019 | NR | Fundus | JPEG | DR grading |
OCTAGON [58] | 213 | DRI OCT Triton (Topcon) | AUR | Spain | 2019 | 213 | OCTA | JPEG & TIFF | DR detection |
ODIR-2019 [59] | 8000 | Fundus camera (Canon), Fundus camera (ZEISS), and Fundus camera (Kowa) | OA | China | 2019 | 5000 | Fundus | JPEG | DR, HT, AMD and Glaucoma |
OIA-DDR [60] | 13,673 | NR | OA | China | 2019 | 9598 | NR | JPEG | DR grading and lesion segmentation |
Zhongshan Hospital and First People’s Hospital [61] | 19,233 | Multiple colour fundus camera | NOA | China | 2019 | 5278 | Fundus | JPEG | DR grading and lesion segmentation |
AGAR300 [62] | 300 | 45° FOV | OA | India | 2020 | 150 | Fundus | JPEG | DR grading and MA detection |
Bahawal Victoria Hospital [57] | 2500 | Vision Star, 24.1 Megapixel Nikon D5200 camera | NOA | Pakistan | 2020 | 500 | Fundus | JPEG | DR grading |
Retinal Lesions [63] | 1593 | Selected from EPACS dataset | AUR | China | 2020 | NR | Fundus | JPEG | DR grading and lesion segmentation |
Dataset of fundus images for the study of DR [64] | 757 | Visucam 500 camera of the Zeiss brand | OA | Paraguay | 2021 | NR | Fundus | JPEG | DR grading |
FGADR [60] | 2842 | NR | OA | UAE | 2021 | NR | Fundus | JPEG | DR and DME grading |
Optos Dataset (Tsukazaki Hospital) [65] | 13,047 | 200 Tx ultra-wide-field device (Optos, UK) | NOA | Japan | NR | 5389 | Fundus | JPEG | DR, Glaucoma, AMD, and other eye diseases |
MESSIDOR 2 [66] | 1748 | Topcon TRC NW6 non-mydriatic retinography 45° FOV | AUR | France | NR | 874 | Fundus | TIFF | DR and DME grading |
Noor hospital [67] | 4142 | Heidelberg SPECTRALIS SD-OCT imaging system | NOA | Iran | NR | 148 | OCT | TIFF | DR detection |
Author, Year | Dataset | Grading Details | Pre-Processing | Method | Accuracy | Sensitivity | Specificity | AUC |
---|---|---|---|---|---|---|---|---|
Abràmoff, 2016 [66] | MESSIDOR 2 | Detect RDR and vtDR | No | DCNN: IDx-DR X2.1. ML: RF | NA | 96.80% | 87.00% | 0.98 |
Chandrakumar, 2016 [90] | EyePACS, DRIVE, STARE | Grade DR based on ICDR scale | Yes | DCNN | STARE and DRIVE: 94% | NA | NA | NA |
Colas, 2016 [91] | EyePACS | Grade DR based on ICDR scale | No | DCNN | NA | 96.20% | 66.60% | 0.94 |
Gulshan, 2016 [69] | EyePACS, MESSIDOR 2 | Detect DR based on ICDR scale, RDR and referable DME | Yes | DCNN | NA | EyePACS: 97.5% | EyePACS: 93.4% | EyePACS: 0.99 |
Wong, 2016 [92] | EYEPACS, MESSIDOR 2 | Detect RDR, Referable DME (RDME) | No | DCNN | NA | 90% | 98% | 0.99 |
Gargeya, 2017 [68] | EyePACS, MESSIDOR 2, eOphtha | Detect DR or non-DR | Yes | DCNN | NA | EyePACS: 94% | EyePACS: 98% | EyePACS: 0.97 |
Somasundaram, 2017 [76] | DIARETDB1 | Detect PDR, NPDR | No | ML: t-SNE and ML-BEC | NA | NA | NA | NA |
Takahashi, 2017 [50] | Jichi Medical University | Grade DR with the Davis grading scale (NPDR, severe DR, PDR) | No | DCNN: Modified GoogLeNet | 81% | NA | NA | NA |
Ting, 2017 [52] | SiDRP | Detect RDR, vtDR, glaucoma, AMD | No | DCNN | NA | RDR: 90.5% vtDR: 100% | RDR: 91.6% vtDR: 91.1% | RDR: 0.93 vtDR: 0.95 |
Quellec, 2017 [81] | EyePACS, eOphta, DIARETDB 1 | Grade DR based on ICDR | Yes | DCNN: L2-regularized o-O DCNN | NA | 94.60% | 77% | 0.955 |
Wang, 2017 [93] | EyePACS, MESSIDOR 1 | Grade DR based on ICDR scale | Yes | Weakly supervised network to classify image and extract high resolution image patches containing a lesion | MESSIDOR 1: RDR: 91.1% | NA | NA | MESSIDOR 1: RDR: 0.957 |
Benson, 2018 [94] | Vision Quest Biomedical database | Grade DR based on ICDR scale + scars detection | Yes | DCNN: Inception v3 | NA | 90% | 90% | 0.95. |
Chakrabarty, 2018 [95] | High-Resolution Fundus (HRF) Images | Detect DR | Yes | DCNN | 91.67% | 100% | 100% | F1 score: 1 |
Costa, 2018 [96] | MESSIDOR 1 | Grade DR based on ICDR scale | No | Multiple Instance Learning (MIL) | NA | NA | NA | 0.9 |
Dai, 2018 [97] | DIARETDB1 | MA, HE, CWS, Ex detection | Yes | DCNN: Multi-sieving CNN(image to text mapping) | 96.10% | 87.80% | 99.70% | F1 score: 0.93 |
Dutta, 2018 [98] | EyePACS | Mild NPDR, Moderate NPDR, Severe NPDR, PDR | Yes | DCNN: VGGNet | 86.30% | NA | NA | NA |
Kwasigroch, 2018 [99] | EyePACS | Grade DR based on ICDR scale | Yes | DCNN: VGG D | 81.70% | 89.50% | 50.50% | NA |
Levenkova, 2018 [78] | UWF (Ultra-Wide Field) | Detect CWS, MA, HE, Ex | No | DCNN, SVM | NA | NA | NA | 0.80 |
Mansour, 2018 [72] | EyePACS | Grade DR based on ICDR scale | Yes | DCNN, ML: AlexNet, LDA, PCA, SVM, SIFT | 97.93% | 100% | 0.93 | NA |
Rajalakshmi, 2018 [7] | Smartphone-based imaging device | Detect DR and vtDR Grade DR based on ICDR scale | No | DCNN | NA | DR: 95.8% vtDR: 99.1% | DR: 80.2% vtDR: 80.4% | NA |
Robiul Islam, 2018 [100] | APTOS 2019 | Grade DR based on ICDR scale | Yes | DCNN: VGG16 | 91.32% | NA | NA | NA |
Zhang, 2018 [101] | EyePACS | Grade DR based on ICDR scale | Yes | DCNN: Resnet-50 | NA | 61% | 84% | 0.83 |
Zhang, 2018 [102] | EyePACS | Grade DR based on ICDR scale | No | DCNN | 82.10% | 76.10% | 0.855 | Kappa score: 0.66 |
Arcadu, 2019 [103] | 7 FOV images of RIDE and RISE datasets | 2 step grading based on ETDRS | No | DCNN: Inception v3 | NA | 66% | 77% | 0.68 |
Bellemo, 2019 [104] | Kitwe Central Hospital, Zambia | Grade DR based on ICDR scale | No | DCNN: Ensemble of Adapted VGGNet & Resenet | NA | RDR: 92.25% vtDR: 99.42% | RDR: 89.04% | RDR: 0.973 vt DR: 0.934 |
Chowdhury, 2019 [105] | EyePACS | Grade DR based on ICDR scale | Yes | DCNN: Inception v3 | 2 Class: 61.3% | NA | NA | NA |
Govindaraj, 2019 [106] | MESSIDOR 1 | Detect DR | Yes | Probabilistic Neural Network | 98% | Almost 90% from chart | Almost 97% from chart | F1 score: almost 0.97 |
Gulshan, 2019 [107] | Aravind Eye Hospital and Sankara Nethralaya, India | Grade DR based on ICDR scale | No | DCNN | NA | Aravind: 88.9% SN: 92.1% | Aravind: 92.2% SN: 95.2% | Quadratic weighted K scores: Aravind: 0.85 SN: 0.91 |
Hathwar, 2019 [108] | EyePACS, IDRID | Detect DR | Yes | DCNN: Xception-TL | NA | 94.30% | 95.50% | Kappa score: 0.88 |
He, 2019 [109] | IDRID | Detect DR grade and DME risk | Yes | DCNN: AlexNet | DR grade: 65% | NA | NA | NA |
Hua, 2019 [83] | Kyung Hee University Medical Center | Grade DR based on ICDR scale | No | DCNN: Tri-SDN | 90.60% | 96.50% | 82.10% | 0.88 |
Jiang, 2019 [110] | Beijing Tongren Eye Center | DR or Non-DR | Yes | DCNN: Inception v3, Resnet152 and Inception-Resnet-v2 | Integrated model: 88.21% | Integrated model: 85.57% | Integrated model: 90.85% | 0.946 |
Li, 2019 [111] | IDRID, MESSIDOR 1 | Grade DR based on ICDR scale | No | DCNN: Attention network based on ResNet50 | DR: 92.6%, DME: 91.2% | DR: 92.0%, DME: 70.8% | NA | DR: 0.96 DME: 0.92 |
Li, 2019 [61] | Shanghai Zhongshan Hospital (SZH) and Shanghai First People’s Hospital (SFPH), China, MESSIDOR 2 | Grade DR based on ICDR scale | Yes | DCNN: Inception v3 | 93.49% | 96.93% | 93.45% | 0.9905 |
Metan, 2019 [112] | EyePACS | Grade DR based on ICDR scale | Yes | DCNN: ResNet | 81% | NA | NA | NA |
Nagasawa, 2019 [113] | Saneikai Tsukazaki Hospital and Tokushima University Hospital, Japan | Detect PDR | Yes | DCNN: VGG-16 | NA | PDR: 94.7% | PDR: 97.2% | PDR: 0.96 |
Qummar, 2019 [114] | EyePACS | Grade DR based on ICDR scale | Yes | DCNN: Ensemble of (Resnet50, Inception v3, Xception, Dense121, Dense169) | 80.80% | 51.50% | 86.72% | F1 score: 0.53 |
Ruamviboonsuk, 2019 [115] | Thailand national DR screening program dataset | Grade DR based on ICDR and detect RDME | No | DCNN | NA | DR: 96.8% | DR: 95.6% | DR: 0.98 |
Sahlsten, 2019 [70] | Private dataset | Detect DR based on multiple grading systems, RDR and DME | Yes | DCNN: Inception-v3 | NA | 89.60% | 97.40% | 0.98 |
Sayres, 2019 [82] | EyePACS | Grade DR based on ICDR | No | DCNN | 88.40% | 91.50% | 94.80% | NA |
Sengupta, 2019 [116] | EyePACS, MESSIDOR 1 | Grade DR based on ICDR scale | Yes | DCNN: Inception-v3 | 90. 4% | 90% | 91.94% | NA |
Ting, 2019 [117] | SiDRP, SiMES, SINDI, SCES, BES, AFEDS, CUHK, DMP Melb, with 2 FOV | Grade DR based on ICDR scale | Yes | DCNN | NA | NA | NA | Detect DR: 0.86 RDR: 0.96 |
Zeng, 2019 [118] | EyePACS | Grade DR based on ICDR scale | Yes | DCNN: Inception v3 | NA | 82.2% | 70.7% | 0.95 |
Ali, 2020 [57] | Bahawal Victoria Hospital, Pakistan. | Grade DR based on ICDR scale | Yes | ML: SMO, Lg, MLP, LMT, Lg employed on selected post-optimized hybrid feature datasets | MLP: 73.73% LMT: 73.00 SLg: 73.07 SMO: 68.60 Lg: 72.07% | NA | NA | MLP: 0.916 LMT: 0.919 SLg: 0.921 SMO: 0.878 Lg: 0.923 |
Araujo, 2020 [119] | EyePACS, MESSIDOR 2, IDRID, DMR, SCREEN-DR, DR1, DRIMDB, HRF | Grade DR based on ICDR scale | Yes | DCNN | NA | NA | NA | Kappa score: EyePAC: 0.74 |
Chetoui, 2020 [26] | EyePACS, MESSIDOR 1, 2, eOphta, UoA-DR from the University of Auckland research, IDRID, STARE, DIARETDB0, 1 | Grade DR based on ICDR scale | Yes | DCNN: Inception-ResNet v2 | 97.90% | 95.80% | 97.10% | 98.60% |
Elswah, 2020 [74] | IDRID | Grade DR based on ICDR scale | Yes | DCNN: ResNet 50 + NN or SVM | NN: 88% SVM: 65% | NA | NA | NA |
Gadekallu, 2020 [71] | DR Debrecen dataset collection of 20 features of MESSIDOR 1 | DR or Non-DR | Yes | DCNN ML: PCA + Firefly | 97% | 92% | 95% | NA |
Gadekallu, 2020 [120] | DR Debrecen dataset | Detect DR | Yes | ML: PCA+ grey wolf optimization (GWO) + DNN | 97.30% | 91% | 97% | NA |
Gayathri, 2020 [121] | MESSIDOR 1, EyePACS, DIARETDB0 | Grade DR based on ICDR scale | NA | Wavelet Transform, SVM, RF | MESSIDOR 1: 99.75% | MESSIDOR 1: 99.8% | MESSIDOR 1: 99.9% | NA |
Jiang, 2020 [122] | MESSIDOR 1 | Image-wise label the presence of MA, HE, Ex, CWS | Yes | DCNN: ResNet 50 based | MA: 89.4% HE: 98.9% Ex: 92.8% CWS: 88.6% Normal: 94.2% | MA: 85.5% HE: 100% Ex: 93.3% CWS: 94.6% Normal: 93.9% | MA: 90.7% HE: 98.6% Ex: 92.7% CWS: 86.8% Normal: 94.4% | MA: 0.94 HE: 1 Ex: 0.97 CWS: 0.97 Normal: 0.98 |
Lands, 2020 [123] | APTOS 2019, APTOS 2015 | Grade DR based on ICDR scale | Ye | DCNN: DensNet 169 | 93% | NA | NA | Kappa score: 0.8 |
Ludwig, 2020 [10] | EyePACS, APTOS, MESSIDOR 2, EYEGO | Detect RDR | Yes | DCNN: DenseNet201 | NA | MESSIDOR 2: 87% | MESSIDOR 2: 80% | MESSIDOR 2: 0.92 |
Majumder, 2020 [15] | EyePACS, APTOS 2019 | Grade DR based on ICDR scale | Yes | CNN | 88.50% | NA | NA | NA |
Memari, 2020 [124] | MESSIDOR 1, HEI-MED | Detect DR | Yes | DCNN | NA | NA | NA | NA |
Narayanan, 2020 [125] | APTOS 2019 | Detect and grade DR based on ICDR scale | Yes | DCNN: AlexNet, ResNe, VGG16, Inception v3 | 98.4% | NA | NA | 0.985 |
Pao, 2020 [84] | EyePACS | Grade DR based on ICDR scale | Yes | CNN: bichannel customized CNN | 87.83% | 77.81% | 93.88% | 0.93 |
Paradisa, 2020 [73] | DIARETDB 1 | Grade DR based on ICDR scale | Yes | ResNet-50 for extraction and SVM, RF, KNN, and XGBoost as classifiers | SVM: 99%, KNN: 100% | SVM: 99%, KNN: 100% | NA | NA |
Patel, 2020 [126] | EyePACS | Grade DR based on ICDR scale | Yes | DCNN: MobileNet v2 | 91.29% | NA | NA | NA |
Riaz, 2020 [80] | EyePACS, MESSIDOR 2 | NA | Yes | DCNN | NA | EyePACS: 94.0% | EyePACS: 97.0% | EyePAC: 0.98 |
Samanta, 2020 [127] | EyePACS | Grade DR based on ICDR scale | Yes | DCNN: DenseNet121 based | 84.1% | NA | NA | NA |
Serener, 2020 [128] | EyePACS, MESSIDOR 1, eOphta, HRF, IDRID | Grade DR based on ICDR scale | Yes | DCNN: ResNet 18 | Country: EyePACS: 65% Continent: EyePACS + HRF: 80% | Country: EyePACS: 17% Continent: EyePACS + HRF: 80% | Country: EyePACS: 89% Continent: EyePACS + HRF: 80% | NA |
Shaban, 2020 [129] | APTOS | Grade DR to non-DR, moderate DR, and severe DR | Yes | DCNN | 88% | 87% | 94% | 0.95 |
Shankar, 2020 [85] | MESSIDOR 1 | Grade DR based on ICDR scale | Yes | DCNN: Histogram-based segmentation + SDL | 99.28% | 98.54% | 99.38% | NA |
Singh, 2020 [130] | IDRID, MESSIDOR 1 | Grade DME in 3 levels | Yes | DCNN: Hierarchical Ensemble of CNNs (HE-CNN) | 96.12% | 96.32% | 95.84% | F1 score: 0.96 |
Thota, 2020 [131] | EyePACS | NA | Yes | DCNN: VGG16 | 74% | 80.0% | 65.0% | 0.80 |
Wang, 2020 [132] | 2 Eye hospitals, DIARETDB1, EyePACS, IDRID | MA, HE, EX | Yes | DCNN | MA: 99.7% HE: 98.4% EX: 98.1% Grading: 91.79% | Grading: 80.58% | Grading: 95.77% | Grading: 0.93 |
Wang, 2020 [133] | Shenzhen, Guangdong, China | Grade DR severity based on ICDR scale and detect MA, IHE, SRH, HE, CWS, VAN, IRMA, NVE, NVD, PFP, VPH, TRD | No | DCNN: Multi-task network using channel-based attention blocks | NA | NA | NA | Kappa score: Grading: 0.80 DR feature: 0.64 |
Zhang, 2020 [134] | 3 Hospitals in China | Classify to retinal tear & retinal detachment, DR and pathological myopia | Yes | DCNN: InceptionResNetv2 | 93.73% | 91.22% | 96.19% | F1 score: 0.93 |
Abdelmaksoud, 2021 [135] | EyePACS, MESSIDOR 1, eOphta, CHASEDB 1, HRF, IDRID, STARE, DIARETDB0, 1 | Yes | U-Net + SVM | 95.10% | 86.10% | 86.80% | 0.91 | |
Bora, 2021 [115] | EyePACS | Grade DR based on ICDR scale | No | DCNN: Inception v3 | NA | NA | NA | Three FOV: 0·79 One FOV: 0·70 |
Gangwar, 2021 [136] | APTOS 2019, MESSIDOR 1 | Grade DR based on ICDR scale | Yes | DCNN: Inception ResNet v2 | APTOS:82.18%MESSIDOR 1: 72.33% | NA | NA | NA |
He, 2021 [137] | DDR, MESSIDOR 1, EyePACS | Grade DR based on ICDR scale | No | DCNN: MobileNet 1 with attention blocks | MESSIDOR 1: 92.1% | MESSIDOR 1: 89.2% | MESSIDOR 1: 91% | F1 score: MESSIDOR 1: 0.89 |
Hsieh, 2021 [32] | National Taiwan University Hospital (NTUH), Taiwan, EyePACS | Detect any DR, RDR and PDR | Yes | DCNN: Inception v4 for any DR and RDR and ResNet for PDR | Detect DR: 90.7% RDR: 90.0% PDR: 99.1% | Detect DR: 92.2% RDR: 99.2% PDR: 90.9% | Detect DR: 89.5% RDR: 90.1% PDR: 99.3% | 0.955 |
Khan, 2021 [138] | EyePACS | Grade DR based on ICDR scale | Yes | DCNN: customized highly nonlinear scale-invariant network | 85% | 55.6% | 91.0% | F1 score: 0.59 |
Oh, 2021 [2] | 7 FOV fundus images of Catholic Kwandong University, South Korea | Detect DR | Yes | DCNN: ResNet 34 | 83.38% | 83.38% | 83.41% | 0.915 |
Saeed, 2021 [139] | MESSIDOR, EyePACS | Grade DR based on ICDR scale | No | DCNN: ResNet GB | EyePACS: 99.73% | EyePACS: 96.04% | EyePACS: 99.81% | EyePACS: 0.98 |
Wang, 2021 [140] | EyePACS, images from Peking Union Medical College Hospital, China | Detect RDR with lesion-based segmentation of PHE, VHE, NV, CWS, FIP, IHE, IRMA and MA, then staging based on ICDR scale | No | DCNN: Inception v3 | NA | EyePACS: 90.60% | EyePACS: 80.70% | EyePACS: 0.943 |
Wang. 2021 [141] | MESSIDOR 1 | Grade DR based on ICDR scale | Yes | DCNN: Multichannel-based GAN with semi super- vision | RDR: 93.2%, DR Grading: 84.23% | RDR: 92.6% | RDR: 91.5% | RDR: 0.96 |
Author, Year | Dataset | Pre-Processing Technique | Method | Accuracy |
---|---|---|---|---|
Datta, 2016 [142] | DRIVE, STARE, DIARETDB0, DIARETDB1 | Yes, Contrast optimization | Image processing | NA |
Lin, 2018 [143] | EyePACS | Yes, Convert to entropy images | DCNN | Original image: 81.8% Entropy images: 86.1% |
Mukhopadhyay, 2018 [144] | Prasad Eye Institute, India | Yes, Local binary patterns | ML: Decision tree, KNN | KNN: 69.8% |
Pour, 2020 [145] | MESSIDOR 1, 2, IDRID | Yes, CLAHE | DCNN: EfficientNet-B5 | NA |
Ramchandre, 2020 [146] | APTOS 2019 | Yes, Image augmentation with AUGMIX | DCNN: EfficientNetb3, SEResNeXt32x4d | EfficientNetb3: 91.4% SEResNeXt32x4d: 85.2% |
Shankar, 2020 [85] | MESSIDOR 1 | Yes, CLAHE | DCNN: Hyperparameter Tuning Inception-v4 (HPTI-v4) | 99.5% |
Bhardwaj, 2021 [147] | DRIVE, STARE, MESSIDOR 1, DIARETDB1, IDRID, ROC | Yes, Image contrast enhancement and OD localization | DCNN: InceptionResNet v2 | 93.3% |
Bilal, 2021 [16] | IDRID | Yes, Adaptive histogram equalization and contrast stretching | ML: SVM + KNN + Binary Tree | 98.1% |
Elloumi, 2021 [148] | DIARETDB1 | Yes, Optic disc location, fundus image partitioning | ML: SVM, RF, KNN | 98.4% |
Author, Year | Dataset | Grading Details | Preprocessing | Method | Accuracy | Sensitivity | Specificity | AUC |
---|---|---|---|---|---|---|---|---|
Eladawi, 2018 [149] | OCTA images, University of Louisville, USA | Detect DR | Yes | ML: Vessel segmentation, Local feature extraction, SVM | 97.3% | 97.9% | 96.4% | 0.97 |
Islam, 2019 [150] | Kermani OCT dataset | NA | Yes | DCNN: DenseNet 201 | 98.6% | 0.986 | 0.995 | NA |
Le, 2020 [151] | Private OCTA dataset | Grade DR | No | DCNN: VGG16 | 87.3% | 83.8% | 90.8% | 0.97 |
Sandhu, 2020 [75] | OCT. OCTA, clinical and demographical data, University of Louisville Clinical Center, USA | Detect mild and moderate DR | Yes | ML: RF | 96.0% | 100.0% | 94.0% | 0.96 |
Liu, 2021 [77] | Private OCTA dataset | Detect DR | Yes | Logistic Regression (LR), LR regularized with the elastic net (LR-EN), SVM and XGBoost | LR-EN: 80.0% | LR-EN: 82.0% | LR-EN: 84.0% | LR-EN: 0.83 |
Author, Year | Dataset | Considered Lesions | Preprocessing | Segmentation Method | Sensitivity/Specificity | AUC |
---|---|---|---|---|---|---|
Imani, 2016 [157] | DIARETDB1, HEI-MED, eOphta | Ex | Yes | Dynamic decision thresholding, morphological feature extraction, smooth edge removal | 89.01%/99.93% | 0.961 |
Shah, 2016 [154] | ROC | MA | Yes | Curvelet transform and rule-based classifier | 48.2%/NA | NA |
Quellec, 2017 [81] | EyePACS, eOphta, DIARETDB1 | CWS, Ex, HE, MA | Yes | DCNN: o-O solution | DIARETDB1: CWS: 62.4%/NA Ex: 55.2%/NA HE: 44.9%/NA MA: 31.6%/NA | EyePACS: 0.955 |
Huang, 2018 [155] | MESSIDOR 1, DIARETDB0, 1 | NV | Yes | ELM | NA/NA | ACC: 89.2% |
Kaur, 2018 [156] | STARE, eOphta, MESSIDOR 1, DIARETDB1, private dataset | Ex, CWS | Yes | Dynamic decision thresholding | 94.8%/99.80% | ACC: 98.43% |
Lam, 2018 [160] | EyePACS, eOphta | Ex, MA, HE, NV | NA | DCNN: AlexNet, VGG16, GoogLeNet, ResNet, and Inception-v3 | NA/NA | EyePACS: 0.99 ACC: 98.0% |
Benzamin, 2018 [161] | IDRID | Ex | Yes | DCNN | 98.29%/41.35% | ACC: 96.6% |
Orlando, 2018 [162] | eOphtha, DIARETDB1, MESSIDOR 1 | MA, HE | Yes | ML: RF | NA/NA | 0.93 |
Eftekhari, 2019 [163] | ROC, eOphta | MA | Yes | DCNN: Two level CNN, thresholded probability map | NA/NA | ROC: 0.660 |
Wu, 2019 [164] | HRF | Blood vessels, optic disc and other regions | Yes | DCNN: AlexNet, GoogleNet, Resnet50, VGG19 | NA/NA | AlexNet: 0.94 ACC: 95.45% |
Yan, 2019 [165] | IDRID | Ex, MA, HE, CWS | Yes | DCNN: Global and local Unet | NA/NA | Ex: 0.889 MA: 0.525 HE: 0.703 CWS: 0.679 |
Qiao, 2020 [166] | IDRID | MA | Yes | DCNN | 98.4%/97.10% | ACC: 97.8% |
Wang, 2021 [141] | EyePACS, images from Peking Union Medical College Hospital | Detect RDR with lesion-based segmentation of PHE, VHE, NV, CWS, FIP, IHE, Ex, MA | No | DCNN: Inception v3 and FCN 32s | PHE: 60.7%/90.9% Ex: 49.5%/87.4% VHE: 28.3%/84.6% NV: 36.3%/83.7% CWS: 57.3%/80.1% FIP: 8.7%/78.0% IHE: 79.8%/57.7% MA: 16.4%/49.8% | NA |
Wei, 2021 [63] | EyePACS | MA, IHE, VHE, PHE, Ex, CWS, FIP, NV | Yes | DCNN: Transfer learning from Inception v3 | NA/NA | NA |
Xu, 2021 [167] | IDRID | Ex, MA, HE, CWS | Yes | DCNN: Enhanced Unet named FFUnet | Ex: 87.55%/NA MA: 59.33%/NA HE: 73.42%/NA CWS: 79.33%/NA | IOU: Ex:0.84 MA: 0.56 HE: 0.73 CWS: 0.75 |
Author, Year | Dataset | Considered Lesions | Pre-Processing | Segmentation Method | Sensitivity | Specificity | AUC |
---|---|---|---|---|---|---|---|
Guo, 2018 [159] | UW-OCTA private dataset | Avascular area | Yes | DCNN | Control: 100.0% Diabetes without DR: 99.0% Mild to moderate DR: 99.0% Severe DR: 100.0% | Control: 84.0% Diabetes without DR: 77.0% Mild to moderate DR: 85.0% Severe DR: 68.0% | ACC:Control: 89.0% Diabetes without DR: 79% Mild to moderate DR: 87% Severe DR: 76.0% |
ElTanboly, 2018 [168] | OCT and OCTA images of University of Louisville | 12 different retinal layers & segmented OCTA plexuses | No | SVM | NA | NA | ACC: 97.0% |
ElTanboly, 2018 [168] | SD-OCT images of KentuckyLions Eye Center | 12 distinct retinal layers | Yes | Statistical analysis and extraction of features such as tortuosity, reflectivity, and thickness for 12 retinal layers | NA | NA | ACC: 73.2% |
Sandhu, 2018 [169] | OCT images of University of Louisville, USA | 12 layers; quantifies the reflectivity, curvature, and thickness | Yes | DCNN: 2 Stage deep CNN | 92.5% | 95.0% | ACC: 93.8% |
Holmberg, 2020 [158] | OCT from Helmholtz Zentrum München, Fundus from EyePACS | Segment retinal thickness map, Grade DR based on ICDR scale | No | DCNN: On OCT: Retinal layer segmentation with Unet On fundus: Self supervised learning, ResNet50 | NA | NA | IOU: on OCT: 0.94 |
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Lakshminarayanan, V.; Kheradfallah, H.; Sarkar, A.; Jothi Balaji, J. Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey. J. Imaging 2021, 7, 165. https://doi.org/10.3390/jimaging7090165
Lakshminarayanan V, Kheradfallah H, Sarkar A, Jothi Balaji J. Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey. Journal of Imaging. 2021; 7(9):165. https://doi.org/10.3390/jimaging7090165
Chicago/Turabian StyleLakshminarayanan, Vasudevan, Hoda Kheradfallah, Arya Sarkar, and Janarthanam Jothi Balaji. 2021. "Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey" Journal of Imaging 7, no. 9: 165. https://doi.org/10.3390/jimaging7090165