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COVID-19 CT scan image classification using EfficientNetB2 with transfer learning and deployment using Streamlit. This project focuses on accurately classifying CT scan images into three categories: COVID-19, Healthy, and Others. Leveraging transfer learning on pretrained EfficientNetB2 models, the classification model achieves robust performance.

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nadyanvl/COVID-Multiclass-App

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COVID-Multiclass-App

Simple Web App using Streamlit -> [COVID Multiclass App] - [COVID Multiclass App HF].

Multiclass image classification and prediction using kaggle dataset.

Steps to run Code

  • Clone the repository
git clone https://github.com/nadyanvl/COVID-Multiclass-App.git
  • Goto the cloned folder.
cd COVID-Multiclass-App
  • make virtual environment (windows)
python -m venv venv
  • install requirements.txt
pip install -r requirements.txt

if error, install requirements manually pip install .....

  • run covid multiclass app
streamlit run covid_multiclass_app.py

About

COVID-19 CT scan image classification using EfficientNetB2 with transfer learning and deployment using Streamlit. This project focuses on accurately classifying CT scan images into three categories: COVID-19, Healthy, and Others. Leveraging transfer learning on pretrained EfficientNetB2 models, the classification model achieves robust performance.

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