Ocular Disease Intelligent Recognition (ODIR) is a structured ophthalmic database of 5,000 patients with age, color fundus photographs from left and right eyes, and doctors' diagnostic keywords from doctors.

However, this is the modified version of the original dataset. Extracting each feature to their corresponding images. Here is the list of features: * Normal (N), * Diabetes (D), * Glaucoma (G), * Cataract (C), * Age related Macular Degeneration (A), * Hypertension (H), * Pathological Myopia (M), * Other diseases/abnormalities (O)

About this Data

Ocular Disease Intelligent Recognition (ODIR) is a structured ophthalmic database of 5,000 patients with age, color fundus photographs from left and right eyes and doctors' diagnostic keywords from doctors.

This dataset is meant to represent ‘‘real-life’’ set of patient information collected by Shanggong Medical Technology Co., Ltd. from different hospitals/medical centers in China. In these institutions, fundus images are captured by various cameras in the market, such as Canon, Zeiss and Kowa, resulting into varied image resolutions. Annotations were labeled by trained human readers with quality control management. They classify patient into eight labels including: - Normal (N), - Diabetes (D), - Glaucoma (G), - Cataract (C), - Age related Macular Degeneration (A), - Hypertension (H), - Pathological Myopia (M), - Other diseases/abnormalities (O)

License

License was not specified on source

Splash Image

Image from Omni Matryx by Pixabay

Normal (N), Diabetes (D), Glaucoma (G), Cataract (C), Age related Macular Degeneration (A), Hypertension & Hypertensive (H), Pathological Myopia (M), Other diseases/abnormalities (O)

Dataset de imágenes del fondo del ojo creado a partir de la unión de otros datasets: - https://www.kaggle.com/datasets/gunavenkatdoddi/eye-diseases-classification (Solo glaucoma) - https://www.kaggle.com/datasets/tanjemahamed/odir5k-classification - ORIGA: https://pubmed.ncbi.nlm.nih.gov/21095735/ - G1020: https://arxiv.org/abs/2006.09158 - https://www.kaggle.com/datasets/deathtrooper/multichannel-glaucoma-benchmark-dataset (Multiple) BEH (Bangladesh Eye Hospital) CRFO-v4
DR-HAGIS
DRISHTI-GS1-TRAIN
DRISHTI-GS1-TEST
EyePACS-AIROGS FIVES
HRF (High Resolution Fundus)
JSIEC-1000 LES-AV
OIA-ODIR-TRAIN
OIA-ODIR-TEST-ONLINE
OIA-ODIR-TEST-OFFLINE
ORIGA-light
PAPILA
REFUGE1-TRAIN (Retinal Fundus Glaucoma Challenge 1 Train)
REFUGE1-VALIDATION (Retinal Fundus Glaucoma Challenge 1 Validation)
sjchoi86-HRF - ACRIMA https://www.kaggle.com/datasets/toaharahmanratul/acrima-dataset - Rim-One https://github.com/miag-ull/rim-one-dl

Para testing se usó un dataset completamente separado: - BEH (Bangladesh Eye Hospital) - ACRIMA TESTING

这是电子科技大学微光工作室机器学习方向招新，第六题所需要用到的数据集🥰

这是一个结构化的眼科数据库，包括5,000名患者的年龄，双眼的彩色眼底照片和医生的诊断关键词（ODIR-5K）。该数据集是上工医疗技术有限公司从中国不同医院/医疗中心收集的“真实”患者信息。在这些机构中，眼底图像由市场上的各种相机捕获，例如Canon，Zeiss和Kowa，因此导致各种各样的图像分辨率。病人的识别信息会被移除。注释由经过培训的人类读者进行标记，并具有质量控制管理。他们将患者分为8个标签，包括正常（N），糖尿病（D），青光眼（G），白内障（C），AMD（A），高血压（H），近视（M）和其他疾病/异常（O）。该数据集的发布遵循中国的道德和隐私规则。

Dataset

This dataset was created by LUCAS CUNHA DE CARVALHO

Contents