RSK World - Medical Imaging Dataset | X-ray CT Scan MRI | Disease Detection | Medical AI | RSK World

X-ray Images CT Scan MRI Images Disease Detection Python Medical AI

This project features a comprehensive Medical Imaging dataset designed for professional medical image analysis, disease detection, computer-aided diagnosis, and healthcare AI applications. The dataset includes X-ray images, CT scan images, and MRI images with diagnostic labels and annotations. Includes powerful Python scripts: load_data.py for loading medical images from different modalities, preprocess.py for image preprocessing and normalization, visualize.py for medical image visualization with annotations. Also includes interactive demo website. The package includes interactive demo website, comprehensive README.md, and MIT License. Perfect for medical researchers, data scientists, students, and developers working on medical AI, healthcare analytics, computer-aided diagnosis, and medical image analysis projects.

If you find this Medical Imaging Dataset useful, you can support with a small contribution.

Secure Fast Trusted

Pay via UPI QR

Scan or tap an amount to auto-generate

₹

Open UPI app

GPay PhonePe Paytm

Download Free Source Code

Dataset Overview

Complete Medical Imaging dataset with X-ray, CT scan, and MRI images with diagnostic labels and annotations for medical image analysis.

X-ray images - Real medical chest X-ray images for disease detection
CT scan images - Computed tomography images for medical analysis
MRI images - Magnetic resonance imaging for diagnostic purposes
Diagnostic labels - Pre-labeled annotations in JSON format
DICOM support - Standard medical imaging format compatibility
Ready for machine learning and medical AI models
Multiple image formats - PNG, JPG, JPEG supported
Multiple Python scripts included for image processing
Perfect for medical AI, disease detection & computer-aided diagnosis applications

Dataset Structure & Files

Well-organized project structure with medical images (X-ray, CT scan, MRI), Python scripts for image loading, preprocessing, visualization, and interactive demo.

data/xray/images/ - X-ray image files in PNG/JPG format
data/xray/labels/ - Diagnostic labels in JSON format for X-ray images
data/ct_scan/images/ - CT scan image files
data/ct_scan/labels/ - Diagnostic labels for CT scan images
data/mri/images/ - MRI image files
data/mri/labels/ - Diagnostic labels for MRI images
scripts/load_data.py - Medical image loading functions
scripts/preprocess.py - Image preprocessing and normalization
scripts/visualize.py - Medical image visualization with annotations
index.html - Interactive demo website
README.md - Comprehensive project documentation
PROJECT_SUMMARY.md - Project details and metadata
requirements.txt - Python dependencies (numpy, pillow, opencv, pydicom)
LICENSE - MIT License file
.gitignore - Git ignore configuration
Consistent directory structure for all modalities
Easy to load with load_data.py script
DICOM format support for medical images
Multiple image formats (PNG, JPG, JPEG)
Image type-based organization (xray, ct_scan, mri)
Label-based annotations for supervised learning
Visualization with annotations support
Preprocessing pipeline ready

Medical Image Analysis & Processing

Complete image processing pipeline with support for image loading, preprocessing, normalization, visualization, and medical image analysis.

Image Loading - Load X-ray, CT scan, and MRI images from organized directories
Image Preprocessing - Normalize, resize, and enhance medical images
Label Loading - Load diagnostic labels and annotations from JSON files
Image Visualization - Display medical images with annotations and overlays
Multi-modality Support - Handle X-ray, CT scan, and MRI images
DICOM Processing - Support for DICOM format medical images
Image Statistics - Calculate image statistics and properties
Annotation Display - Visualize diagnostic labels and bounding boxes
Modality-specific Processing - Custom processing for each image type
Batch Processing - Process multiple images at once
Image Format Conversion - Convert between different image formats
Image Enhancement - Contrast adjustment, noise reduction, and filtering
Region of Interest (ROI) - Extract and analyze specific image regions
Medical Image Quality - Assess image quality and artifacts
Disease Detection Ready - Preprocessed images for ML model training
Visualization Tools - Interactive image viewer with zoom and pan
Label Validation - Verify and validate diagnostic labels
Data Export - Export processed images and annotations
Summary Reports - Generate image analysis summaries

Multiple Image Formats

Dataset available in multiple image formats for maximum compatibility with different medical imaging tools and ML frameworks.

PNG format - Portable Network Graphics for high-quality medical images
JPG/JPEG format - JPEG images for compressed medical imaging
DICOM format - Standard medical imaging format support via pydicom
JSON labels - Structured diagnostic labels and annotations
NumPy array compatible - Easy conversion to numpy arrays for ML
PIL/Pillow ready - Direct loading with Python Imaging Library
OpenCV compatible - Ready for computer vision and image processing
TensorFlow/PyTorch ready - Can be converted for deep learning models
Medical imaging libraries ready - Compatible with SimpleITK, nibabel
Standard medical formats - Widely supported medical image formats
Easy to import and process - Simple image loading functions
Compatible with all ML libraries - Universal format support
Jupyter Notebook ready - Perfect for interactive medical image analysis
Python image processing ready - Native PIL and OpenCV support
Image augmentation ready - Compatible with albumentations, imgaug
Medical imaging tools ready - Compatible with ITK, VTK
API integration ready - JSON format for labels and metadata
Image validation support - Easy to validate image quality and format
DICOM ready - Proper DICOM format support for medical imaging

Analysis & Visualization

Comprehensive image visualization tools with interactive medical image viewer and analysis capabilities.

Interactive Medical Image Viewer - Image display with zoom and pan
Multiple Image Display - View X-ray, CT scan, and MRI images
Image gallery - Browse through medical images by modality
Annotation overlay - Display diagnostic labels on images
Image comparison - Compare multiple medical images side-by-side
Image statistics visualization - Display image properties and metadata
Label visualization - Show bounding boxes and annotations
Modality-based filtering - Filter images by type (X-ray, CT, MRI)
Image metadata display - Show DICOM metadata and image information
Region of Interest (ROI) highlighting - Highlight specific image regions
Image enhancement visualization - Compare original and processed images
Dataset statistics - Comprehensive summary of image dataset
Interactive image viewer - Zoom, pan, and navigate medical images
Label distribution charts - Visualize diagnostic label frequencies
Image quality assessment - Display image quality metrics
Modality distribution - Show distribution of image types
Image preview grid - Grid view of medical images
Export functionality - Download images and visualizations
Responsive design - Works on desktop, tablet, and mobile devices

Compatible Frameworks

Works with all major medical imaging and deep learning frameworks out of the box.

Scikit-learn ML library - Classification, clustering, preprocessing
Convolutional Neural Networks - CNN models for medical image classification
Deep Learning - TensorFlow, PyTorch, Keras compatibility
Medical Imaging Libraries - SimpleITK, nibabel, pydicom support
Image Processing - PIL/Pillow, OpenCV for image manipulation
NumPy numerical computing - Array operations for image arrays
Image augmentation - albumentations, imgaug for data augmentation
matplotlib visualization - Static image visualization and plots
Medical visualization - 3D Slicer, ITK-SNAP compatibility
DICOM processing - pydicom for medical image format support
Computer vision (OpenCV) - Image processing and analysis
Medical AI frameworks - MONAI, nnU-Net compatibility
Jupyter Notebook support - Interactive medical image analysis
Google Colab ready - Works in cloud-based notebooks
VS Code integration - Python extension support
PyCharm compatible - Full IDE support
Medical prediction models - Custom models for disease detection
Medical imaging tools - Image registration, segmentation support
Transfer learning ready - Pre-trained medical imaging models
R/RStudio compatible - Cross-platform medical image analysis
PACS integration - DICOM network protocol support

What You Get

Complete package with all files needed for professional medical image analysis, disease detection, computer-aided diagnosis, and medical AI projects.

Medical images - X-ray, CT scan, and MRI images with diagnostic labels
Multiple modalities - X-ray, CT scan, and MRI image types
Python image loading script - load_data.py with comprehensive loading functions
load_data.py - Medical image loading with support for X-ray, CT scan, and MRI
preprocess.py - Image preprocessing with normalization, resizing, and enhancement
visualize.py - Medical image visualization with annotations and overlays
Diagnostic labels - JSON format annotations for all images
Organized directory structure - Separate folders for each modality
index.html - Interactive demo website
Multiple image formats - PNG, JPG, JPEG supported
DICOM support - Standard medical imaging format compatibility
Complete documentation - README.md, PROJECT_SUMMARY.md
Documentation files - Comprehensive guides and project information
requirements.txt - All Python dependencies listed and versioned (numpy, pillow, opencv, pydicom)
LICENSE - MIT License (free for commercial and non-commercial use)
Ready-to-use code examples - Copy and run scripts immediately
Modality-based organization - Separate directories for X-ray, CT scan, and MRI
Label-based annotations - Diagnostic labels in JSON format
Image preprocessing pipeline - Ready-to-use preprocessing functions
Visualization tools - Interactive medical image viewer
Medical AI ready - Preprocessed images for model training

Interactive Demo Website

Beautiful demo website with medical image explorer, image gallery, and comprehensive guide.

Modern animated design - Smooth transitions and visual effects
Interactive Medical Image Explorer - Browse and view medical images
Image Gallery - Display X-ray, CT scan, and MRI images
Image Viewer - Zoom, pan, and navigate medical images
Image Metrics - Visual representation of image properties
Filter by modality - Filter images by type (X-ray, CT scan, MRI)
Image visualization - Display images with diagnostic labels
Image distribution - Modality-based image breakdown
Dataset statistics display - Total images, modalities, label counts
Interactive image display - Click to view full-size images
Step-by-step usage guide - Comprehensive instructions
Dark theme with gradients - Modern, professional appearance
Fully responsive layout - Mobile, tablet, and desktop support
Image export options - Download images and annotations
Python scripts download - Access to all analysis scripts
Interactive filters - Filter by modality, label type
Image detail view - Individual image display with metadata
Statistics summary - Quick overview of dataset metrics
No backend required - Pure HTML, CSS, JavaScript
Cross-browser compatible - Works on Chrome, Firefox, Safari, Edge

Python Scripts Included

Professional Python scripts for medical image loading, preprocessing, visualization, and medical image analysis.

load_data.py - Comprehensive medical image loading script
preprocess.py - Image preprocessing with normalization and enhancement
visualize.py - Medical image visualization with annotations
Image loading functions - Load X-ray, CT scan, and MRI images
DICOM loading support - Load DICOM format medical images
Label loading functions - Load diagnostic labels from JSON files
Image preprocessing functions - Normalize, resize, and enhance images
Image statistics functions - Calculate image properties and metadata
Visualization functions - Display images with annotations and overlays
Modality-specific processing - Custom processing for each image type
Batch processing support - Process multiple images efficiently
Image format conversion - Convert between different image formats
Annotation display - Visualize diagnostic labels and bounding boxes
Medical image utilities - Custom functions for medical image processing
Dataset verification - Image format checking, validation, and quality assessment
Export functionality - Export processed images and annotations
Report generation - Generate image analysis reports with insights
Error handling - Comprehensive error checking and informative messages
Code comments and documentation - Well-documented code for learning
Complete code examples - Ready-to-run scripts with examples
Modular design - Reusable functions for different image processing tasks
Best practices - Follows Python coding standards (PEP 8)
Image visualization - Display medical images with zoom and pan support
Image metadata extraction - Extract and display DICOM metadata