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Aydinhamedi · Jan 10, 2024 · efe214e · efe214e
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 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![CodeQL](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/codeql.yml/badge.svg?branch=main)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/codeql.yml)
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+[![Dependency Review](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/dependency-review.yml/badge.svg)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/dependency-review.yml)\
+[![Python application](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app.yml/badge.svg)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app.yml)
 ### This project uses a deep learning model built with the TensorFlow framework to detect pneumonia in X-ray images. The model architecture is based on the EfficientNetB7 model, which has achieved an accuracy of approximately 97.12% (97.11538%) on our test data. This high accuracy rate is one of the strengths of our AI model.
 > [!IMPORTANT]
 > The code that have achived the highest acc is `backup/V6/Model_T&T.ipynb`.\