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🏥 Health Imaging RAG Database

A comprehensive Retrieval-Augmented Generation (RAG) database for medical image analysis, built using DenseNet-121 deep learning model and FAISS vector search.

Developed by: Gaston Software Solutions LLP
Location: Ntinda, Kampala, Uganda
Contact: info@gss-tec.com
Website: www.gss-tec.com

📊 Database Overview

Statistics

Total Images: 66,828 medical images
Embedding Dimension: 1,024 features per image
Model: DenseNet-121 (pretrained on ImageNet)
Vector Index: FAISS (GPU-accelerated)

Dataset Distribution

Dataset	Images	Classes	Description
Malaria	55,116	2	Cell images for malaria detection
Pneumonia	11,712	2	Chest X-rays for pneumonia diagnosis
Total	66,828	4	Combined medical imaging database

🔬 Disease Classes

1. Malaria Detection (55,116 images)

Parasitized: 27,558 images - Cells infected with malaria parasites
Uninfected: 27,558 images - Healthy blood cells

2. Pneumonia Detection (11,712 images)

NORMAL: 3,166 images - Healthy chest X-rays
PNEUMONIA: 8,546 images - Chest X-rays showing pneumonia

📁 Database Files

The RAG database consists of three essential files:

1. `embeddings.npy` (261.05 MB)

Format: NumPy array
Shape: (66828, 1024)
Content: DenseNet-121 feature vectors for all images
Purpose: Dense vector representations for similarity search

2. `metadata.json` (17.20 MB)

Format: JSON
Content: Image metadata including:
- image_path: Original file path
- class: Disease classification
- dataset: Source dataset name
- filename: Image filename
Purpose: Linking search results back to original images

3. `faiss_index.bin` (261.05 MB)

Format: FAISS binary index
Type: IndexFlatIP (Inner Product)
Content: Optimized vector search index
Purpose: Fast similarity search across 66K+ images

🚀 Usage

Loading the Database

import numpy as np
import faiss
import json
from pathlib import Path

# Load embeddings
embeddings = np.load('embeddings.npy')

# Load metadata
with open('metadata.json', 'r') as f:
    metadata = json.load(f)

# Load FAISS index
index = faiss.read_index('faiss_index.bin')

print(f"Loaded {index.ntotal} vectors")

Performing Similarity Search

from PIL import Image
import torchvision.transforms as transforms
import torchvision.models as models
import torch

# Load DenseNet-121 model
model = models.densenet121(weights=models.DenseNet121_Weights.IMAGENET1K_V1)
model.classifier = torch.nn.Identity()
model.eval()

# Image preprocessing
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                       std=[0.229, 0.224, 0.225])
])

# Extract features from query image
def extract_features(image_path):
    img = Image.open(image_path).convert('RGB')
    img_tensor = transform(img).unsqueeze(0)
    
    with torch.no_grad():
        features = model(img_tensor).numpy()
        # Normalize
        features = features / np.linalg.norm(features)
    return features

# Search for similar images
query_features = extract_features('query_image.jpg')
k = 5  # Number of similar images to retrieve

distances, indices = index.search(query_features, k)

# Get results
for i, idx in enumerate(indices[0]):
    result = metadata[idx]
    print(f"{i+1}. {result['class']} - {result['filename']}")
    print(f"   Similarity: {distances[0][i]:.4f}")

🧠 Model Architecture

DenseNet-121

Architecture: Dense Convolutional Network
Layers: 121 layers with dense connections
Feature Dimension: 1,024
Pretrained: ImageNet-1K
Advantages:
- Better gradient flow through dense connections
- Parameter efficiency
- Strong feature extraction for medical images
- Proven performance on healthcare datasets

📈 Performance Characteristics

Search Performance

Index Type: Flat (exact search)
Distance Metric: Inner Product (cosine similarity)
Search Speed: ~1ms per query (GPU)
Accuracy: 100% (exact nearest neighbors)

Memory Requirements

Total Size: ~539 MB (all three files)
RAM for Loading: ~600 MB
GPU Memory: ~500 MB (for model inference)

🔧 Technical Details

Feature Extraction Pipeline

Input: RGB medical images (variable sizes)
Preprocessing:
- Resize to 256×256
- Center crop to 224×224
- Normalize with ImageNet statistics
Model: DenseNet-121 (without classification layer)
Output: 1,024-dimensional feature vector
Normalization: L2 normalization for cosine similarity

Vector Index

Algorithm: FAISS IndexFlatIP
Normalization: L2-normalized vectors
Similarity: Cosine similarity via inner product
GPU Support: Yes (for faster search)

📚 Use Cases

1. Medical Image Retrieval

Find similar medical images based on visual features:

Retrieve similar disease presentations
Find reference cases for diagnosis
Compare patient scans with historical data

2. Diagnostic Support

Assist healthcare professionals:

Identify similar cases from database
Provide visual references for rare conditions
Support differential diagnosis

3. Research & Analysis

Enable medical research:

Analyze disease patterns across large datasets
Study visual similarities in medical conditions
Build training datasets for ML models

4. Educational Tools

Support medical education:

Create case study collections
Build interactive learning tools
Demonstrate disease variations

🔒 Data Privacy & Ethics

Important Considerations

Public Datasets: All images from publicly available Kaggle datasets
De-identification: Ensure patient privacy in any deployment
Clinical Use: This is a research tool, not for clinical diagnosis
Validation: Always validate results with medical professionals
Bias: Be aware of potential dataset biases

🛠️ Building the Database

The database was built using:

Notebook: Health_Imaging_RAG_Colab.ipynb
Platform: Google Colab with T4 GPU
Processing Time: ~30-40 minutes
Framework: PyTorch + FAISS

Datasets Sources (Kaggle)

paultimothymooney/chest-xray-pneumonia
iarunava/cell-images-for-detecting-malaria

📊 Future Enhancements

Planned Additions

Skin disease images
Brain tumor MRI scans
Tuberculosis chest X-rays
Additional disease categories

Technical Improvements

Implement IVF index for faster search at scale
Add metadata filtering capabilities
Support for multi-modal queries
Real-time feature extraction API

📖 Citation

If you use this database in your research, please cite:

@software{health_imaging_rag_2026,
  author = {{Gaston Software Solutions LLP}},
  title = {Health Imaging RAG Database},
  year = {2026},
  publisher = {GSS-LLP},
  address = {Ntinda, Kampala, Uganda},
  url = {https://www.gss-tec.com},
  note = {Medical image retrieval system using DenseNet-121 and FAISS}
}

Please also cite the original datasets:

@dataset{pneumonia_xray,
  author = {Paul Mooney},
  title = {Chest X-Ray Images (Pneumonia)},
  year = {2018},
  publisher = {Kaggle},
  url = {https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia}
}

@dataset{malaria_cells,
  author = {Arunava},
  title = {Cell Images for Detecting Malaria},
  year = {2019},
  publisher = {Kaggle},
  url = {https://www.kaggle.com/datasets/iarunava/cell-images-for-detecting-malaria}
}

📞 Contact & Support

Gaston Software Solutions LLP

Email: info@gss-tec.com
Website: www.gss-tec.com
Location: Ntinda, Kampala, Uganda

For technical support, questions, or collaboration opportunities, please contact us via email.

⚖️ License

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Intellectual Property Notice

This Health Imaging RAG Database is provided by Gaston Software Solutions LLP (GSS-LLP) under the MIT License for open-source use.

While the software is open-source and freely available for use, modification, and distribution under the MIT License terms, the intellectual property rights, including the database architecture, processing pipeline, and implementation methodology, remain the property of Gaston Software Solutions LLP.

Key Points:

✅ Free to use, modify, and distribute under MIT License
✅ Open-source for research and commercial applications
✅ No warranty or liability from GSS-LLP
⚠️ Intellectual property and methodology rights retained by GSS-LLP
⚠️ Original datasets subject to their respective licenses
⚠️ Medical use requires appropriate validation and regulatory compliance

Dataset Licenses

The underlying medical image datasets are sourced from Kaggle and are subject to their respective licenses. Users must comply with the original dataset licenses when using this RAG database. Please refer to:

Pneumonia X-Ray Dataset: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia
Malaria Cells Dataset: https://www.kaggle.com/datasets/iarunava/cell-images-for-detecting-malaria

🌟 About Gaston Software Solutions LLP

Gaston Software Solutions LLP (GSS-LLP) is a technology company based in Ntinda, Kampala, Uganda, specializing in AI/ML solutions, software development, and data science applications for healthcare and other industries.

Our Mission: Leveraging cutting-edge technology to solve real-world problems and improve lives through innovative software solutions.

Services:

AI/ML Model Development
Healthcare Technology Solutions
Custom Software Development
Data Science Consulting
RAG Systems & Vector Databases

Visit us at www.gss-tec.com or contact info@gss-tec.com for more information.

Last Updated: June 2026
Version: 1.0
Total Images: 66,828
Model: DenseNet-121
Developed by: Gaston Software Solutions LLP
License: MIT

Downloads last month: 12

Total file size:

566 MB