🔬 Deep Learning Project Spotlight: Chest Disease Classification from Chest CT Scan Images 🌡️📷

I'm thrilled to showcase an impressive deep learning project focused on the classification of chest diseases using chest CT scan images! 🏥🖥️

The project "Chest Disease Classification from Chest CT Scan Image" employs advanced deep learning techniques to analyze and classify various chest diseases based on CT scan images. This groundbreaking work has the potential to revolutionize medical diagnostics and improve patient care. 💪💻

By leveraging the power of deep learning algorithms, the project aims to accurately identify and classify different chest conditions, including pneumonia, lung cancer, tuberculosis, and more. Its innovative approach can assist radiologists and healthcare professionals in making faster and more precise diagnoses, leading to timely interventions and improved treatment outcomes. 🩺⚕️

The project utilizes state-of-the-art deep learning frameworks and architectures to train a robust model capable of effectively distinguishing between different chest diseases. Through extensive data preprocessing, feature extraction, and model training, the system achieves remarkable accuracy and reliability in disease classification. 📊🔍

This project serves as a testament to the potential of deep learning in medical imaging and highlights the transformative impact it can have on healthcare. By automating the analysis of CT scan images, it reduces the burden on medical professionals and enhances the efficiency of disease diagnosis. 🌟💡

To explore this inspiring project further, check out the GitHub repository: Chest-Disease-Classification-from-Chest-CT-Scan-Image 📁🔗

Stay tuned for more groundbreaking advancements in deep learning and its applications in the medical field! 🚀🧠

#DeepLearning #MedicalImaging #ChestDiseaseClassification #CTScanImages #HealthcareAI

Chest-Disease-Classification-from-Chest-CT-Scan-Image

[tensorflow installation installation on macOS] 
https://developer.apple.com/metal/tensorflow-plugin/

[Data link] (https://drive.google.com/file/d/1RE-2BwQGjQdUao9F8pvGEbZe2wZHFQVQ/view?usp=sharing)

[Dataset Link] (https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images)

[Youtube link added my me] (https://youtu.be/4wdKIKt9HH4)

[Youtube link for Cuda by Krish naik] (https://www.youtube.com/watch?v=StH5YNrY0mE)

Workflows

1. Update config.yaml
2. Update params.yaml
3. Update the entity
4. Update the configuration manager in src config
5. Update the components
6. Update the pipeline
7. Update the main.py
8. Update the dvc.yaml

Git commands

git add .

git commit -m "Updated"

git push origin main

How to run?

conda create -n chest python=3.9 -y

conda activate chest

pip install -r requirements.txt

# to execute application
python app.py

python main.py

#chest-Disease-Classification-from-Chest-CT-Scan-Image.mlflow

MLFLOW_TRACKING_URI=https://dagshub.com/mayankchugh-learning/Chest-Disease-Classification-from-Chest-CT-Scan-Image.mlflow \
MLFLOW_TRACKING_USERNAME=mayankchugh-learning \
MLFLOW_TRACKING_PASSWORD=##

export MLFLOW_TRACKING_URI=https://dagshub.com/mayankchugh-learning/Chest-Disease-Classification-from-Chest-CT-Scan-Image.mlflow
export MLFLOW_TRACKING_USERNAME=mayankchugh-learning
llm("what is yolo?")
MLFLOW_TRACKING_PASSWORD= 

#Experiment Demo

MLFLOW_TRACKING_URI=https://dagshub.com/mayankchugh-learning/MLFLowExperimentDemo.mlflow \export MLFLOW_TRACKING_URI=https://dagshub.com/mayankchugh-learning/MLFLowExperimentDemo.mlflow
MLFLOW_TRACKING_USERNAME=mayankchugh-learning \
MLFLOW_TRACKING_PASSWORD= \
python script.py

export MLFLOW_TRACKING_URI=https://dagshub.com/mayankchugh-learning/MLFLowExperimentDemo.mlflow
export MLFLOW_TRACKING_USERNAME=mayankchugh-learning
export MLFLOW_TRACKING_PASSWORD=

DVC cmd

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

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🔬 Deep Learning Project Deployment on AWS Cloud: Chest Disease Classification from Chest CT Scan Images 📊🩺

I am to announce the successful deployment of our latest deep learning project deployment on AWS Cloud! 🚀🌐

I have also created a YouTube video showcasing the project and its functionalities. Watch it here: YouTube video link1 YouTube video link2

LinkedIn Profile

Project Overview: I have developed a state-of-the-art system for classifying Chest Diseases from Chest CT Scan Images using deep learning. This solution leverages cutting-edge technologies and tools to provide accurate and efficient diagnoses, aiding in the early detection and treatment of chest diseases.

Technologies Used for CI/CD: To ensure seamless integration and continuous delivery of the project, I utilized a robust CI/CD pipeline. The following technologies played a pivotal role:

1️⃣ Jenkins: Our reliable automation server, Jenkins, enabled us to automate our project's building, testing, and deployment, ensuring efficiency and reliability.

2️⃣ GitHub: As the foundation of our version control system, GitHub provided us with collaborative features, enabling seamless teamwork and efficient code management.

3️⃣ AWS ECR: Amazon Elastic Container Registry (ECR) facilitated our Docker container images' secure storage and management, ensuring easy access and scalability.

4️⃣ AWS ElastiCache: I leveraged AWS ElastiCache to enhance the application's performance and scalability by caching frequently accessed data, reducing latency, and improving response times.

5️⃣ AWS EC2: Amazon EC2 served as the backbone of our project, providing scalable and reliable computing capacity in the cloud. It allowed us to deploy and run applications quickly.

Development Technologies: During the development phase, we utilized a robust set of technologies and tools, including:

✅ Python: The core programming language for deep learning projects, Python, offers flexibility, robustness, and an extensive library ecosystem.

✅ Jupyter Notebook: Jupyter Notebook provided an interactive and exploratory development environment, allowing us to prototype and experiment efficiently.

✅ Visual Studio Code: As our preferred integrated development environment (IDE), It enhanced our coding experience with its rich features and seamless integration with other tools.

✅ MLflow: MLflow was our reliable platform for managing the machine learning lifecycle. It enabled us to track experiments, package code, and deploy models easily.

✅ DVC: Data Version Control (DVC) helped us manage large datasets efficiently by providing a Git-like interface for data versioning and collaboration.

hashtag#DeepLearning hashtag#AWS hashtag#ChestDiseaseClassification hashtag#CTScan hashtag#HealthcareAI hashtag#CI/CD hashtag#GitHub hashtag#Jenkins hashtag#AWS-ECR hashtag#AWS-EB hashtag#AWS-EC2 hashtag#Python hashtag#JupyterNotebook hashtag#VSCode hashtag#MLflow hashtag#DVC