Image Dataset on Eye Diseases Classification with Symptoms and SMOTE Validation
Image and symptom dataset for normal, uveitis, conjunctivitis, cataract, and eyelid-drooping classification, with source-reported SMOTE balancing to 649 images per class.
At a glance
| Field | Value |
|---|---|
| Short name | uveitis_smote |
| Full name | Image Dataset on Eye Diseases Classification with Symptoms and SMOTE Validation |
| Primary category | multimodal |
| Contained modalities | external_eye, tabular |
| Tasks | classification, text_generation |
| Samples | 3,245 |
| Classes | Not reported (Not reported) |
| Splits | all |
| Size | 0.5 GB |
| Source-stated terms | CC BY 4.0 |
| Normalized terms | cc-by |
| Descriptive screening label | Standard label without an explicit NC clause; not a permission finding |
| Terms scope | dataset_files |
| Access friction | anonymous_direct |
| Route backend | Mendeley Data |
| Availability | available (checked 2026-07-21) |
| Acquisition support | standard_platform_supported |
| Legacy sample-loader status | Metadata and access only |
Notes
Web-sourced images and SMOTE-based synthetic balancing are reported. Use original images, not synthetic oversampling, for clinical validation where possible.
Access preflight and acquisition
- CLI
- Python
# Read-only preflight
eyehub download uveitis_smote --data-dir ./data --dry-run --json
# Explicit transfer, only when preflight reports supported behavior
eyehub download uveitis_smote --data-dir ./data
from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY
ds = REGISTRY.get_dataset('uveitis_smote')
print(preflight_dataset(ds, './data')) # no transfer
Upstream page: data.mendeley.com/datasets
Source-term evidence: data.mendeley.com/datasets
Loader status
This catalog record provides metadata and access instructions, but it does not yet include a standard DatasetSample loader. Inspect the source file structure or contribute a loader before using it in a training pipeline.
Citation
- BibTeX
- Plain text
@misc{uveitis_smote,
title = { Image Dataset on Eye Diseases Classification with Symptoms and SMOTE Validation },
note = { Bitto AK, Ahmed M. Image Dataset on Eye Diseases Classification (Uveitis, Conjunctivitis, Cataract, Eyelid) with Symptoms and SMOTE Validation. Mendeley Data, V2, 2024. doi:10.17632/n9zp473wfw.2 },
year = { 2024 },
url = { https://data.mendeley.com/datasets/n9zp473wfw/2 },
}
Bitto AK, Ahmed M. Image Dataset on Eye Diseases Classification (Uveitis, Conjunctivitis, Cataract, Eyelid) with Symptoms and SMOTE Validation. Mendeley Data, V2, 2024. doi:10.17632/n9zp473wfw.2
Source-stated terms
- Raw source string: CC BY 4.0
- Normalized category:
cc-by - Apparent scope:
dataset_files - Descriptive screening label: Standard label without an explicit NC clause; not a permission finding
⚠️ Source-stated terms, scope, and normalized labels are curation metadata, not legal advice or a permission finding. Review the current official source before transfer or reuse.
Related datasets with shared modalities
- lmod_plus: LMOD+ Multimodal Ophthalmology Benchmark (32,633 records,
unknown) - eed_astig: EED-Astig Pediatric External-Eye Dataset (3,088 records,
research-only) - ocular_chat_vqa: OcularChat-VQA: AREDS-Derived Patient-Physician Dialogue Dataset (844,000 records,
cc-by-nc-sa) - eyecare_100k: Eyecare-100K: Multimodal Ophthalmology VQA Corpus (102,000 records,
unknown) - x_pcr: X-PCR Ophthalmology Progressive Clinical Reasoning Benchmark (18,700 records,
unknown) - oculoscope: OculoScope: Fairer AI in Ophthalmology Dataset (16,530 records,
cc-by) - popeye_nir: PopEYE Infrared Ocular Image Dataset (14,976 records,
cc-by) - leops_erg: LEOPs Light-Adapted Electroretinogram and Oscillatory Potentials Dataset (9,743 records,
cc-by)