Conjunctival melanoma detection using deep learning in smartphone images

Human/derived image or image-annotation observations. from Human ocular anterior-segment images gathered from public web sources; source-level provenance remains limited.

At a glance

Field	Value
Short name	mendeley_conjunctival_melanoma_detection_using_deep_learning
Full name	Conjunctival melanoma detection using deep learning in smartphone images
First published	2020-09-01
Publication date precision	day
Publication date evidence	data.mendeley.com/datasets
Publication date source field	citation_publication_date (version 1)
Publication date reviewed	2026-09-11
Primary category	external_eye
Resource role	current_dataset
Dataset family	mendeley_conjunctival_melanoma_detection_using_deep_learning
Contained modalities	external_eye, tabular
Tasks	classification
Primary reported quantity	Not reported
Classes	Not reported (Not reported)
Splits	all
Size	Not reported
Source-stated terms	CC BY-NC 3.0
Normalized terms	cc-by-nc
Descriptive screening label	Explicit noncommercial clause recorded; check source
Terms scope	dataset_files
Access friction	self_service_authenticated
Route backend	Mendeley Data
Availability	available (checked 2026-08-02)
Acquisition support	standard_platform_supported
Legacy sample-loader status	Metadata and access only

Reported quantities

No reproducible primary item count was exposed for the cataloged source version.

Counts retain their source-reported units. Additional rows can describe components, paired items, or derivative copies and are not automatically added to the primary quantity.

Notes

Human provenance: Human ocular anterior-segment images gathered from public web sources; source-level provenance remains limited. Source-review finding: Indexed record describes manually clinician-classified smartphone ocular images.

Access information and download

CLI

# Read-only preflight
eyehub download mendeley_conjunctival_melanoma_detection_using_deep_learning --data-dir ./data --dry-run --json

# Download, only when preflight reports supported behavior
eyehub download mendeley_conjunctival_melanoma_detection_using_deep_learning --data-dir ./data

Python

from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY

ds = REGISTRY.get_dataset('mendeley_conjunctival_melanoma_detection_using_deep_learning')
print(preflight_dataset(ds, './data'))  # no download

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

@misc{mendeley_conjunctival_melanoma_detection_using_deep_learning,
  title  = { Conjunctival melanoma detection using deep learning in smartphone images },
  note   = { Conjunctival melanoma detection using deep learning in smartphone images. Mendeley Data, V2. doi:10.17632/t75wjsw6bw.2 },
  url    = { https://data.mendeley.com/datasets/t75wjsw6bw/2 },
}

Plain text

Conjunctival melanoma detection using deep learning in smartphone images. Mendeley Data, V2. doi:10.17632/t75wjsw6bw.2.

Source-stated terms

• Raw source string: CC BY-NC 3.0
• Normalized category: cc-by-nc
• Apparent scope: dataset_files
• Descriptive screening label: Explicit noncommercial clause recorded; check source

⚠️ 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.

Similar resources by shared modality

• lmod_plus: LMOD+ Multimodal Ophthalmology Benchmark (32,633 annotated instances, unknown)
• uveitis_smote: Image Dataset on Eye Diseases Classification with Symptoms and SMOTE Validation (3,245 images, cc-by)
• eed_astig: EED-Astig Pediatric External-Eye Dataset (3,088 images, research-only)
• dryad_sbcc2fr6n: Exploring phenotypic diversity of pigmented traits and iris features in Pakistani population (514 participants, cc0)
• dryad_iris_surface_features: Iris Surface Features Dataset (Not reported, cc0)
• mendeley_application_machine_learning_detecting_iron_deficiency: Application of Machine Learning in Detecting Iron Deficiency Anemia Using Conjunctiva image Dataset from Ghana (Not reported, cc-by)
• mendeley_cp_anemic_conjunctival_pallor_ghana: CP-AnemiC (A Conjunctival Pallor) Dataset from Ghana (Not reported, cc-by)
• mendeley_nuclear_cataract_database_biomedical_machine_learning: Nuclear Cataract Database for Biomedical and Machine Learning Applications (Not reported, cc-by)