Glaucoma Classification Datasets
A comprehensive collection of high-resolution fundus images, including optic disc and surrounding structures, that can be used for early glaucoma detection and classification.
- Labeling Type: Is_Glaucoma
- Data Format: Image
- Data Type: Synthetic Data
Main product
Data Quantity (Samples)
Total Price
$ 8,700
(VAT Included)Looking for custom-made dataset or researcher-accessible data? Please contact us for inquiries.
About Dataset
1) Data Introduction
• The Glaucoma Classification Datasets is a collection of fundus image datasets designed for the early detection and classification of glaucoma. Each image is labeled with a binary class indicating the presence or absence of glaucoma (Glaucoma / Normal).2) Data Utilization
(1) Characteristics of the Glaucoma Classification Datasets: • The dataset integrates five major publicly available fundus image datasets: DRISHTI-GS, RIM-ONE, ACRIMA, ORIGA, and G1020. • It consists of high-resolution images that capture abnormalities in the optic disc and surrounding structures, making it suitable for glaucoma diagnosis. (2) Applications of the Glaucoma Classification Datasets: • Development of automated glaucoma classification models: The dataset can be used to train deep learning models that automatically classify normal and glaucomatous eyes based on morphological features such as optic disc size and vascular patterns. • Early diagnosis support systems: The dataset can help build AI-powered early warning systems that visually detect early signs of glaucoma.Meta Data
Normal
Outstanding
Data Samples 5
Utility
| Downstream Classification (▲) | KID (▼) | One Class Classification (▼) | |
|---|---|---|---|
| Total | 0 | 0 | 0 |
| Suitability | OK | OK | OK |
The higher the value, the better (▲)
Model Performance
Downstream classification accuracy is an indicator used to evaluate the usefulness of synthetic data. It measures whether synthetic data performs similarly to real data. The method involves training the same model separately on real data and synthetic data, and then comparing the accuracies of the two models. Interpretation: A high accuracy rate means that the model trained on synthetic data performs similarly to the one trained on real data, indicating that the synthetic data is of high quality and well represents the real data.
The closer to zero or the lower the value, the better (▼)
Quality
KID (Kernel Inception Distance) is a metric used to evaluate the similarity between generated images and real images. It compares the differences between the two sample distributions using Kernel Mean Embedding, without assuming a normal distribution. Interpretation: A lower KID score suggests that generated images are more similar to real images, with a score close to 0 being ideal. Specifically, a score below 0.01 indicates very high similarity.
Privacy
| LPIPS (▲) | SSIM (▼) | |
|---|---|---|
| Total | 0 | 0 |
| Suitability | OK | OK |
The higher the value, the better (▲)
Perceptual Similarity
Learned Perceptual Image Patch Similarity (LPIPS) is a metric used to measure the visual similarity between two images by utilizing neural networks to extract key features and calculate the distance between them. High LPIPS value: Indicates high similarity between images, raising the risk of information leakage. Low LPIPS value: Suggests that synthetic images are perceptually different from real images, indicating a lower risk of sensitive information leakage.
The closer to zero or the lower the value, the better (▼)
Structural Similarity
The Structural Similarity Index Measure (SSIM) is a metric used to assess the similarity between two images. It is primarily used to compare the quality of a restored or compressed image with the original image. SSIM measures visual similarity by considering brightness, contrast, and structure.
High SSIM value (0.9 or above): Indicates that the synthetic image is very similar to the real image, which may increase the risk of information leakage.
Low SSIM value (0.6 or below): Indicates low similarity and reduced risk of leakage.
Premium Report Information
Premium dataset sample
