DOCNET AI Models

Welcome to the DOCNET AI Models Repository — a collection of advanced machine learning models powering remote healthcare and telemedicine solutions at DOCNET.

Our mission is to make AI-driven diagnostics accessible, affordable, and reliable across the globe — enabling doctors and patients to detect critical diseases early, regardless of their location.

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🩺 Overview

This repository contains trained and experimental AI models developed by the DOCNET AI Research Team.
Each model targets a specific medical condition and is organized in its own directory for clarity and modularity.

Current Models

Model Name	Description	Primary Use
🧠 brain_tumor_predictor	Deep learning model for detecting and classifying brain tumors from MRI scans.	Early detection and diagnosis support for neurological patients.
🦠 malaria_predictor	Image-based CNN model trained on blood smear images to detect malaria parasites.	Fast and accurate malaria screening in remote clinics.
🩹 skin_cancer_predictor	Model trained on dermatological images to identify various types of skin lesions and cancers.	Teledermatology and skin health assessment.

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🧩 Repository Structure

Each model follows a standard structure to ensure consistency and scalability:

model_name/
│
├── notebooks/ # Jupyter notebooks for data exploration, training, and evaluation
├── models/ # Serialized model files (e.g., .h5, .pkl, .pt)
└── README.md # Model-specific documentation (optional)

Example:

model_name/
│
├── notebooks/ # Jupyter notebooks for data exploration, training, and evaluation
├── models/ # Serialized model files (e.g., .h5, .pkl, .pt)
└── README.md # Model-specific documentation (optional)

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⚙️ Getting Started

Prerequisites

• Python 3.9+
• Jupyter Notebook or JupyterLab
• Recommended dependencies:

  pip install -r requirements.txt
  
  

Running a Model

To explore or test a model:

1. Navigate to the desired model directory.

2. Open the relevant notebook in notebooks/.

3. Run the notebook to reproduce the training or prediction process.

🧬 Research & Development

Our models are built using state-of-the-art machine learning frameworks such as:

• TensorFlow / Keras

• PyTorch

• scikit-learn

• OpenCV

• NumPy / Pandas

Each model is continuously refined through real-world medical data partnerships and internal validation to ensure high clinical accuracy.

🌍 About DOCNET

DOCNET is a digital health company redefining telemedicine and AI-powered remote diagnostics across Africa and beyond. We focus on leveraging artificial intelligence to empower medical professionals and expand access to healthcare in underserved regions.

"Our vision is a world where no patient is left undiagnosed because of distance."

🤝 Contributing

We welcome contributions from researchers, healthcare professionals, and engineers.

To contribute:

1. Fork this repository.

2. Create a new branch (feature/your-feature-name).

3. Submit a pull request describing your changes.

Please ensure all contributions follow DOCNET’s AI Model Development Guidelines.