肺炎检测AI：基于深度学习的医学影像分析项目

# Pneumonia Detection AI 🤖 <img src="https://img.shields.io/badge/Python-FFD43B?style=for-the-badge&logo=python&logoColor=blue"/> <img src="https://img.shields.io/badge/Jupyter-F37626.svg?&style=for-the-badge&logo=Jupyter&logoColor=white"/> <img src="https://img.shields.io/badge/TensorFlow-FF6F00?style=for-the-badge&logo=tensorflow&logoColor=white"/> <img src="https://img.shields.io/badge/Keras-FF0000?style=for-the-badge&logo=keras&logoColor=white"/> <img src="https://img.shields.io/badge/OpenCV-27338e?style=for-the-badge&logo=OpenCV&logoColor=white"/> [](https://opensource.org/licenses/MIT) [](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/codeql.yml) [](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/dependency-review.yml)\ [![Python Test [main]](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app.yml/badge.svg?branch=main)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app.yml) [![Python Test [Beta-b]](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app_Beta-b.yml/badge.svg?branch=Beta-b)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app_Beta-b.yml)\ [![Python Test [Alpha-b]](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app_Alpha-b.yml/badge.svg?branch=Alpha-b)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app_Alpha-b.yml) ### 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 had achieved the highest acc is `backup/V6/Model_T&T.ipynb`.\ > And the codes with the light and super light models are `backup/V7/Model_T&T.ipynb`, `backup/V8/Model_T&T.ipynb`. ## Usage > [!TIP] > If you just want the model go to the Github Releases. > [!NOTE] > This model was built using the [EfficientNet ](https://github.com/qubvel/efficientnet) library, > which is a collection of state-of-the-art models for image classification. To use the model, > you need to install the library and import it as follows: > ```python > import efficientnet.tfkeras > ``` > Or clone the project and use `Utils.FixedDropout`: > ```python > from Utils.FixedDropout import FixedDropout > from keras.models import load_model > > # Load the model > model = load_model('PAI_model_T.h5', custom_objects={'FixedDropout': FixedDropout}) > ``` 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 ``` Additionally, a Graphical User Interface (GUI) is available. you can fined the GUI in ``` Interface\GUI ``` ### Example Image of the CLI (V0.8.9.3) ⤵  ### Example Image of the GUI (V0.8.9.6) ⤵  ## Release > ### Newest release 📃 > #### [Go to newest release](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/releases/latest) ## Training System Specifications - **Graphics Card (GPU)**: RTX 3090 - **Memory (RAM)**: 64GB - **Operating System (OS)**: Windows 11 Pro - **Processor (CPU)**: Intel Core i7-12700KF ## Model The model is a Convolutional Neural Network (CNN) trained on a dataset of 23681 X-ray images. The dataset is a combination of the following: - Chest X-ray Pneumonia dataset from Kaggle - Covid19-Pneumonia-Normal Chest X-Ray Images from Mendeley - RSNA dataset This combined dataset provides a comprehensive set of images for training the model.\ ### Model list: | Model | Base Model | Params | acc | Status | |----------|-----------------|--------|--------|--------| | V6 | efficientnet-b7 | 65.4M | 97.12% | ✅ | | V7 light | efficientnet-b4 | 29.7M | 97.12% | ✅ | | V8 Super light | efficientnet-b0 | 4.8M | 96.47% | ✅ | ## Training Methods ### The AI model supports two distinct training approaches: - rev1: A straightforward method using Keras fit function for basic training. - rev2: An enhanced training strategy incorporating data augmentation and subset training for improved accuracy and generalization. ### rev2 Training Simplified: - Memory Optimization: Begins with clearing system memory to ensure efficient resource utilization. - Hyperparameter Setup: Configures essential training parameters such as epoch count and batch size. - Data Enrichment: Utilizes data augmentation techniques to introduce variability in the training dataset. - Focused Training: Implements training on data subsets to reduce overfitting and streamline the learning process. - Adaptive Learning Rate: Applies a dynamic learning rate schedule to fine-tune the training progression. - Training Supervision: Uses callbacks for monitoring training, saving the best model, and enabling early stopping. - Progressive Learning: Trains the model iteratively on subsets, evaluating and adjusting after each epoch. - Data Standardization: Normalizes image inputs to facilitate model training. - Robustness Enhancement: Introduces random noise to training images to strengthen model robustness against unseen data. - While rev1 is suitable for quick and simple model training, rev2 is tailored for those seeking a more sophisticated and potentially more effective training regimen. ## Repository Structure Please note that due to the large size of some files and folders, they are not available directly in the repository. However, they can be found in the [Releases](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/releases) page of the repository. This includes the model weights and the database, which are crucial for the functioning of the AI model. ## Contribution Any contributions to improve the project are welcome. You can submit a pull request or open an issue on GitHub. Please make sure to test your changes thoroughly before submitting. We appreciate your help in making this project better. ## WARNING > [!CAUTION] The model provided in this project should not be used for medical diagnosis without further validation. While the model has shown high accuracy in detecting pneumonia from X-ray images, it is not a substitute for professional medical advice. Please consult with a healthcare professional for medical advice. ## Other > [!NOTE] > Please note that this code uses my: > - Python-CLI-template > - for more info go to https://github.com/Aydinhamedi/Python-CLI-template. > - Python-color-print-V2 > - for more info go to https://github.com/Aydinhamedi/Python-color-print-V2. > - Python-color-print > - for more info go to https://github.com/Aydinhamedi/Python-color-print. ## Results > [!WARNING] > Results were achieved using Rev2 training method and Rev1.2 model and > with `backup/V6/Model_T&T.ipynb` code. <!-- #### N/A --> ### Acc:  ### Grad cam: | Model | Grad-cam Ex | |----------|----------| | V6 | | | V7 light | 🚧None🚧| | V8 super light | 🚧None🚧| ### Other:  <!--