Kidney-Classy

Project Description

Kidney-Classy is an AI-based system designed to classify kidney diseases from medical images using deep learning. The project implements a complete machine learning workflow including data ingestion, model training, evaluation, and deployment through a web application.

The system leverages TensorFlow/Keras for building the deep learning model, MLflow for experiment tracking, and DVC (Data Version Control) for managing datasets and pipeline stages. A Flask API is used to provide endpoints for training and prediction, making it easy to interact with the model.

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Key Features

• Deep learning model for kidney disease image classification
• End-to-end machine learning pipeline
• Experiment tracking using MLflow
• Data and pipeline versioning with DVC
• REST API built with Flask
• Simple web interface for interaction

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Technology Stack

• Python
• TensorFlow / Keras
• Flask
• MLflow
• DVC
• NumPy / Pandas

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Project Setup

1. Clone the Repository

https://github.com/awithaaa/Kidney-Classy
cd Kidney-Classy

2. Create a Conda Environment

conda create -n kidney python=3.13 -y
conda activate kidney

3. Install Required Packages

pip install -r requirements.txt

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Running the Project

Before using the prediction endpoint, the model must be trained.

Train the Model

Trigger the training pipeline through the API:

curl -X POST http://localhost:8080/train

This command runs the training pipeline configured with DVC and logs experiments using MLflow.

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Start the Application

python app.py

Once the server starts, open the following address in your browser:

http://localhost:8080

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API Endpoints

Endpoint	Method	Description
/	GET	Loads the web interface
/train	POST	Runs the training pipeline
/predict	POST	Upload an image and receive prediction

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Machine Learning Pipeline

The Kidney-Classy project follows a structured ML pipeline consisting of several stages:

1. Data Ingestion Collects and prepares the dataset required for training.

2. Base Model Preparation Defines the deep learning architecture used for classification.

3. Model Training Trains the neural network using the processed dataset.

4. Model Evaluation Evaluates model performance and records metrics using MLflow.

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Experiment Tracking

All training experiments are logged using MLflow, allowing you to track:

• Training parameters
• Model metrics
• Model versions
• Experiment history

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Data Version Control

This project uses DVC to manage datasets and pipeline stages. It helps maintain reproducibility and version control for machine learning workflows.

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