Blood–Liver–Kidney Tri-Organ Imaging Dataset
Description
A multi-modal medical imaging dataset for object detection and tissue classification. It includes three datasets: blood cell detection (object detection), liver tissue analysis (classification), and kidney tissue analysis (classification), designed for multi-sample correlation analysis. Blood Cell Detection Dataset Dataset Type: Object Detection Total Images: 874 images Total Annotations: 4,888 labeled objects Classes: 3 classes RBC (Red Blood Cells) - Class ID: 1 WBC (White Blood Cells) - Class ID: 2 Platelets - Class ID: 0 Data Split: Training: 70% (765 images) Validation: 10% (73 images) Testing: 20% (36 images) Annotation Format: Normalized bounding box coordinates Format: class_id x_center y_center width height All coordinates normalized to 0-1 range Each image contains at least one annotated object (no null examples) Image Specifications: Format: JPG/JPEG Minimum Resolution: 640x640 pixels Recommended Resolution: 1024x1024+ pixels Supported Formats: JPG, PNG Use Cases: Automated blood cell counting, hematology analysis, diagnostic assistance, medical image analysis research Liver Tissue Analysis Dataset Dataset Type: Tissue Classification Total Images: 1,000 images Classes: 3 classes Hepatocytes (Class ID: 0) - Main functional cells of the liver responsible for protein synthesis, metabolism, and detoxification Sinusoids (Class ID: 1) - Small blood vessels in the liver that allow blood to flow through the liver tissue Portal Triads (Class ID: 2) - Structural units containing portal vein, hepatic artery, and bile duct branches Class Distribution: Hepatocytes: 461 samples Sinusoids: 296 samples Portal Triads: 243 samples Data Split: Training: 70% (700 images) Validation: 15% (150 images) Testing: 15% (150 images) Image Specifications: Minimum Resolution: 512x512 pixels Recommended Resolution: 1024x1024 pixels Supported Formats: JPG, PNG, TIFF Minimum Images per Class: 300 Recommended Images per Class: 1,000 Annotation Format: Class-based classification or segmentation masks Expert-validated annotations Label extraction from directory structure or filename convention Use Cases: Liver histopathology analysis, disease diagnosis, tissue structure identification, computational pathology research Kidney Tissue Analysis Dataset Dataset Type: Tissue Classification Total Images: 1,000 images Classes: 2 classes Glomeruli (Class ID: 0) - Network of capillaries in the kidney that filter blood to form urine Tubules (Class ID: 1) - Small tubes in the kidney that reabsorb water and nutrients from filtered blood Class Distribution: Glomeruli: 503 samples Tubules: 497 samples Data Split: Training: 70% (700 images) Validation: 15% (150 images) Testing: 15% (150 images) Image Specifications: Minimum Resolution: 512x512 pixels Recommended Resolution: 1024x1024 pixels Supported Formats: JPG, PNG, TIFF Minimum Images per Class: 300 Recommended Images per Class: 1,000 Annotation Format: Class-based classification or segmentation masks Expert-validated annotation