https://github.com/bessouat40/python-ai-explainability
A python project to find anomaly in an Xray image.
https://github.com/bessouat40/python-ai-explainability
deep-learning explainable-ai health python tensorflow
Last synced: over 1 year ago
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A python project to find anomaly in an Xray image.
- Host: GitHub
- URL: https://github.com/bessouat40/python-ai-explainability
- Owner: Bessouat40
- Created: 2023-12-09T07:52:21.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-05T17:35:13.000Z (over 2 years ago)
- Last Synced: 2025-01-17T01:49:00.640Z (over 1 year ago)
- Topics: deep-learning, explainable-ai, health, python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 54.4 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# python AI explainability
A python project to find anomaly in an Xray image.
The aim is detect pneumonia in a thorax radiography and explain AI decision.
**_Training data source :_**
[database link](https://www.kaggle.com/datasets/tolgadincer/labeled-chest-xray-images)
## Model
I use a `VGG16` model.
## M1 use
You need to create a conda environment to increase your training performances :
```bash
source ~/.zshrc
conda create -n tf_m1 python=3.11
conda activate tf_m1
conda install -c apple tensorflow-deps
pip install tensorflow-macos
pip install tensorflow-metal
```
## Training
First create `.env` file :
```bash
mv .env.example .env
```
Then fill `.env` file with your values.
In your conda env :
```bash
python train.py
```
## Training results with test set
```bash
-------------------- Dataset Summary --------------------
Number of train images : 4684
Number of test images : 586
Number of validation images : 586
Shape of each images : (224, 224, 3)
---------------------------------------------------------
loss: 0.0611 - accuracy: 0.9795
```
## Results
## Tensorboard
**_Source :_**
[tensorboard-doc](https://www.tensorflow.org/tensorboard/get_started?hl=fr)
### For python notebook
```python
%load_ext tensorboard
%tensorboard --logdir logs/fit
```