Kidney Disease Classification using CNN and VGG16 Architecture

Our model predicts the presence of kidney tumors based on given CT-scan images, determining whether a person has a tumor or not.

Workflows

1. Update config.yaml
2. Update secrets.yaml [Optional]
3. Update params.yaml
4. Update the entity
5. Update the configuration manager in src config
6. Update the components
7. Update the pipeline
8. Update the main.py
9. Update the dvc.yaml
10. app.py

How to run?

STEPS:

Clone the repository

[https://github.com/HimanshuRajput013/Kidney_disease_predication_using_deep_learning]

STEP 01- Create a conda environment after opening the repository

conda create -n env python=3.8 -y

conda activate env

STEP 02- install the requirements

pip install -r requirements.txt

# Finally run the following command
python app.py

Now,

open up you local host and port

MLflow

• Documentation

cmd

• mlflow ui

dagshub

dagshub

MLFLOW_TRACKING_URI=
MLFLOW_TRACKING_USERNAME=
MLFLOW_TRACKING_PASSWORD=
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/HimanshuRajput013/Kidney_disease_predication_using_deep_learning.mlflow

export MLFLOW_TRACKING_USERNAME=HimanshuRajput013

export MLFLOW_TRACKING_PASSWORD=

DVC cmd

1. dvc init
2. dvc repro
3. dvc dag

About MLflow & DVC

MLflow

• Its Production Grade
• Trace all of your expriements
• Logging & taging your model

DVC

• Its very lite weight for POC only
• lite weight expriements tracker
• It can perform Orchestration (Creating Pipelines)