Kidney Tumor, Cyst, or Stone Classification

Project Overview

The main goal of this project is to develop a reliable and efficient deep-learning model that can accurately classify kidney tumors and Stone from medical images.

Introduction

Kidney Disease Classification is a project utilizing deep learning techniques to classify Kidney Tumor and Stone diseases from medical images dataset. This project leverages the power of Deep Learning, Machine Learning Operations (MLOps) practices, Data Version Control (DVC). It integrates with DagsHub for collaboration and versioning.

Dagshub Project Pipeline

Mlflow Stats

Importance of the Project

Enhancing Healthcare: By providing accurate and quick disease classification, this project aims to improve patient care and diagnostic accuracy significantly.
Research and Development: It serves as a tool for researchers to analyze medical images more effectively, paving the way for discoveries in the medical field.
Educational Value: This project can be a learning platform for students and professionals interested in deep learning and medical image analysis.

Technical Overview

Deep Learning Frameworks: Utilizes popular frameworks like TensorFlow or PyTorch for building and training the classification models.
Data Version Control (DVC): Manages and versions large datasets and machine learning models, ensuring reproducibility and streamlined data pipelines.
Git Integration: For source code management and version control, making the project easily maintainable and scalable.
MLOps Practices: Incorporates best practices in machine learning operations to automate workflows, from data preparation to model deployment.
DagsHub Integration: Facilitates collaboration, data and model versioning, experiment tracking, and more in a user-friendly platform.

How to run?

STEPS:

Clone the repository

https://github.com/krishnaik06/Kidney-Disease-Classification-Deep-Learning-Project

STEP 01- Create a conda environment after opening the repository

conda create -n venv python=3.11 -y

conda activate venv

STEP 02- install the requirements

pip install -r requirements.txt

# Finally run the following command
python app.py

Now,

open up your local host and port

To Run the Pipeline

dvc repro

This project is still in development, and we welcome contributions of all kinds: from model development and data processing to documentation and bug fixes.

Join me in this exciting journey to revolutionize the field of medical image classification with AI!