Blood Cell images for Cancer detection Dataset
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A comprehensive collection of high-resolution blood cell images for cancer detection, comprising both normal and abnormal cells.
- Labeling type: cell type
- Data Format: Image
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About Dataset
1) Data Introduction
• The Blood Cell images for Cancer detection Dataset was constructed by collecting blood cell images to support early leukemia diagnosis. It includes both normal and abnormal cells, and is based on high-resolution images that precisely capture cellular morphology, making it suitable for medical image analysis and AI training applications.2) Data Utilization
(1) Characteristics of the Blood Cell images for Cancer detection Dataset: • Composed of images that reflect fine morphological differences between cells, supporting the early detection and diagnosis of leukemia and various other blood-related diseases. • Applicable for differentiating normal and abnormal cells, classifying leukemia subtypes, and monitoring disease progression for clinical use. (2) Applications of the Blood Cell images for Cancer detection Dataset: • Development of Automated Cell Classification Models: The dataset can be used to train AI models that automatically classify normal and abnormal cells by learning morphological features such as nuclear shapes and cytoplasmic characteristics. • Leukemia Subtype Identification and Diagnostic Support: By analyzing the characteristics of specific cell types such as myeloblasts and lymphoblasts, the dataset can support distinguishing between AML (acute myeloid leukemia) and ALL (acute lymphoblastic leukemia) and enable early diagnosis.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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