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!