Kidney Disease Classification Project 🩺🧬

Project Overview

This is a deep learning project focused on classifying kidney diseases using advanced machine learning techniques. The project leverages state-of-the-art deep learning models to assist in early detection and diagnosis of kidney-related medical conditions.

🚀 Key Features

• Advanced Deep Learning Classification
• MLflow Experiment Tracking
• DVC (Data Version Control) Integration
• Flask Web Application
• Comprehensive Model Evaluation

🛠 Tech Stack

• Deep Learning: TensorFlow 2.12.0
• Data Manipulation: Pandas, NumPy
• Visualization: Matplotlib, Seaborn
• Experiment Tracking: MLflow 2.2.2
• Version Control: DVC
• Web Framework: Flask

🔧 Installation

Prerequisites

• Anaconda or Miniconda
• Python 3.8

Setup Steps

1. Clone the repository:

git clone https://github.com/LohiyaH/Kidney-Disease-Classification-Deep-Learning-Project
cd Kidney-Disease-Classification-Deep-Learning-Project

2. Create Conda Environment:

conda create -n kidney-disease-cls python=3.8 -y
conda activate kidney-disease-cls

3. Install Dependencies:

pip install -r requirements.txt

🏃‍♂️ Running the Application

Start Web Application

python app.py

MLflow Experiment Tracking

mlflow ui

📂 Project Structure

• src/: Source code modules
• research/: Experimental notebooks
• config/: Configuration files
• templates/: Web application templates
• model/: Saved model artifacts

🤝 Workflow

1. Update configuration files
2. Prepare data
3. Train model
4. Evaluate performance
5. Track experiments with MLflow
6. Deploy web application

📊 Model Performance

• Tracked experiments available via MLflow
• Detailed performance metrics in scores.json

🔬 Research & Development

Explore experimental work in the research/ directory. Jupyter notebooks provide insights into model development.

📜 License

This project is open-sourced. Check LICENSE for details.

🐛 Issues & Contributions

Please report issues or submit pull requests on GitHub.

📞 Contact

Developed by Harsh