# Ophthalmic Disease Classification on EDD

Source: [Ophthalmic Disease Classification on EDD](<https://www.sota2.com/research/sota/ophthalmic-disease-classification-on-edd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-18T06:28:22.005Z

Evaluation of Ophthalmic Disease Classification performance on the EDD dataset, reporting AUC, F1, Precision, and Recall.

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | Ophthalmic Disease Classification |
| Dataset (including any stated split) | EDD |
| Metric | Precision |
| Unit | PERCENT |
| Direction | Higher is better |
| Results loaded | 4 of 4 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [Precision](<https://www.sota2.com/research/sota/ophthalmic-disease-classification-on-edd.md?metric=Precision>): Higher is better; unit: PERCENT.

- [AUC](<https://www.sota2.com/research/sota/ophthalmic-disease-classification-on-edd.md?metric=AUC>): Higher is better; unit: PERCENT.

- [F1 Score](<https://www.sota2.com/research/sota/ophthalmic-disease-classification-on-edd.md?metric=F1%20Score>): Higher is better; unit: PERCENT.

- [Recall](<https://www.sota2.com/research/sota/ophthalmic-disease-classification-on-edd.md?metric=Recall>): Higher is better; unit: PERCENT.

## Recorded results

| Method | Precision | Result date | Report paper |
| --- | --- | --- | --- |
| MobileNet and DenseNet121 | 98.45 | Not recorded | 68fa7a461e209131cea82244 |
| EfficientNet-B5 | 96.08 | Not recorded | 68fa7a461e209131cea82244 |
| EfficientNetB0, VGG-16, and VGG-19 | 93.92 | Not recorded | 68fa7a461e209131cea82244 |
| ML-CNN | 91.5 | Not recorded | 68fa7a461e209131cea82244 |

### MobileNet and DenseNet121

Precision: 98.45. Result ID: 6a0ab17247f34ce924f01fbc.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7a461e209131cea82244.md>)

Recorded evaluation configuration: {"Venue(Year)":"Diagnostics(2023)"}. This record does not assert independent reproduction.

### EfficientNet-B5

Precision: 96.08. Result ID: 6a0ab17247f34ce924f01fbf.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7a461e209131cea82244.md>)

Recorded evaluation configuration: {"Venue(Year)":"Engineering, Technology & Applied Science Research(2024)"}. This record does not assert independent reproduction.

### EfficientNetB0, VGG-16, and VGG-19

Precision: 93.92. Result ID: 6a0ab17247f34ce924f01fbd.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7a461e209131cea82244.md>)

Recorded evaluation configuration: {"Venue(Year)":"ELECO(2023)"}. This record does not assert independent reproduction.

### ML-CNN

Precision: 91.5. Result ID: 6a0ab17247f34ce924f01fbe.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7a461e209131cea82244.md>)

Recorded evaluation configuration: {"Venue(Year)":"Electronics(2022)"}. This record does not assert independent reproduction.

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