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Aydinhamedi authored Feb 5, 2024
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
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### 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.
### This project uses a deep learning model built with the TensorFlow Library 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`.\
> And the code with the light model is `backup/V7/Model_T&T.ipynb`.
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> [!TIP]
> If you just want the model go to the Github Releases.
The project includes a Command Line Interface (CLI) for easy use of the model. The CLI, which is based on the [Python CLI template](https://github.com/Aydinhamedi/Python-CLI-template) from the same author, provides a user-friendly, colorful interface that allows you to interact with the model. you can fined the CLI in
The project includes a Command Line Interface (CLI) and a (GUI) Graphical User Interface for easy use of the model. The CLI, which is based on the [Python CLI template](https://github.com/Aydinhamedi/Python-CLI-template) from the same author, provides a user-friendly, colorful interface that allows you to interact with the model. you can fined the CLI in

```
Interface\CLI
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- Covid19-Pneumonia-Normal Chest X-Ray Images from Mendeley
- RSNA dataset

This combined dataset provides a comprehensive set of images for training the model.
This combined dataset provides a comprehensive set of images for training the model.\

### Model list:
| Model | Base Model | Params | acc |
|----------|-----------------|--------|--------|
| V6 | efficientnet-b7 | 65.4M | 97.12% |
| V7 light | efficientnet-b4 | 29.7M | 97.12% |

## Training Methods
### The AI model supports two distinct training approaches:
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### Acc:
![img_](doc/V6/D1.png)
### Grad cam:
![img_](doc/V6+/D1.png)
![img_](doc/V6+/D2.png)
![img_](doc/V6+/D3.png)
| Model | Grad-cam Ex |
|----------|----------|
| V6 | ![img_](doc/V6+/D1.png)![img_](doc/V6+/D2.png)![img_](doc/V6+/D3.png)|
| V7 light | 🚧None🚧|
### Other:
![img_](doc/V6/D4.png)
<!--
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