eye diseases classification Dataset
A comprehensive collection of retina-focused images, providing visual features that can be used to train AI models for eye disease classification.
- Labeling Type: Eye health
- Data Format: Image
- Data Type: Synthetic Data
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Data Quantity (Samples)
Total Price
$ 9,300
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About Dataset
1) Data Introduction
• The eye_diseases_classification dataset is a computer vision dataset designed for retinal image-based classification of eye diseases. It is divided into four categories: Normal, Diabetic Retinopathy, Cataract, and Glaucoma.2) Data Utilization
(1) Characteristics of the eye_diseases_classification dataset: • The dataset consists of retina-focused images, providing appropriate visual features for training AI models to detect and classify eye diseases. (2) Applications of the eye_diseases_classification dataset: • Development of eye disease classification models: This dataset can be used to train deep learning-based image classification models that automatically distinguish between normal and pathological cases across four disease types. • Medical image analysis research: It can be applied to various experiments in medical AI, such as improving model accuracy, analyzing visual patterns, and visualizing attention maps for interpretability.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.
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