NephroNet-VGG16: Kidney Disease Classification Using VGG16

NephroNet-VGG16 is a deep learning project aimed at classifying kidney diseases from CT scan images using the VGG16 convolutional neural network model. This project leverages the power of VGG16's pre-trained architecture to accurately detect and categorize kidney diseases, providing a valuable tool for medical professionals and researchers.

Features : Pre-trained VGG16 Model: Utilizes the VGG16 model pre-trained on ImageNet for feature extraction and fine-tuning on kidney CT scan images. Data Augmentation: Implements various data augmentation techniques to enhance the robustness and generalizability of the model. High Accuracy: Achieves high classification accuracy through extensive training and validation processes. User-Friendly Interface: Provides a straightforward interface for loading images, predicting results, and visualizing outcomes.

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/Saniya-BZ/NephroNet-VGG16

STEP 01- Create a conda environment after opening the repository

python -m venv venv

venv\Scripts\activate

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

cmd

• mlflow ui

dagshub

dagshub

Run this to export as env variables:

export MLFLOW_TRACKING_URI= "URI"
export MLFLOW_TRACKING_USERNAME="USER NAME"
export MLFLOW_TRACKING_PASSWORD="PASSWORD"
python script.py

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)

AWS-CICD-Deployment-with-Github-Actions

1. Login to AWS console.

2. Create IAM user for deployment

#with specific access

1. EC2 access : It is virtual machine

2. ECR: Elastic Container registry to save your docker image in aws


#Description: About the deployment

1. Build docker image of the source code

2. Push your docker image to ECR

3. Launch Your EC2 

4. Pull Your image from ECR in EC2

5. Lauch your docker image in EC2

#Policy:

1. AmazonEC2ContainerRegistryFullAccess

2. AmazonEC2FullAccess

3. Create ECR repo to store/save docker image

- Save the URI: 566373416292.dkr.ecr.us-east-1.amazonaws.com/chicken

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

#optinal

sudo apt-get update -y

sudo apt-get upgrade

#required

curl -fsSL https://get.docker.com -o get-docker.sh

sudo sh get-docker.sh

sudo usermod -aG docker ubuntu

newgrp docker

6. Configure EC2 as self-hosted runner:

setting>actions>runner>new self hosted runner> choose os> then run command one by one

7. Setup github secrets:

AWS_ACCESS_KEY_ID=

AWS_SECRET_ACCESS_KEY=

AWS_REGION = us-east-1

AWS_ECR_LOGIN_URI = 

ECR_REPOSITORY_NAME = simple-app