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https://github.com/muhammedhossam/pneumonia-detection-
A deep learning-based project for classifying chest X-ray images to detect pneumonia
https://github.com/muhammedhossam/pneumonia-detection-
binary-classification cnn computer-vision data-preprocessing deep-learning image-classification imbalanced-data keras machine-learning medical-imaging pneumonia-detection tensorflow xray-images
Last synced: about 1 month ago
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A deep learning-based project for classifying chest X-ray images to detect pneumonia
- Host: GitHub
- URL: https://github.com/muhammedhossam/pneumonia-detection-
- Owner: muhammedhossam
- Created: 2024-12-24T14:19:28.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-12-24T14:45:25.000Z (about 1 month ago)
- Last Synced: 2024-12-24T15:37:16.882Z (about 1 month ago)
- Topics: binary-classification, cnn, computer-vision, data-preprocessing, deep-learning, image-classification, imbalanced-data, keras, machine-learning, medical-imaging, pneumonia-detection, tensorflow, xray-images
- Language: Jupyter Notebook
- Homepage:
- Size: 31.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# XRayNet: Pneumonia Detection Using Deep Learning
This repository contains a Jupyter Notebook implementing a deep learning pipeline for classifying chest X-ray images to detect pneumonia. It uses TensorFlow and Keras for model development and evaluation.
## Features
- **Data Preprocessing**: Loads and preprocesses chest X-ray images, converting them to grayscale and resizing them.
- **Data Augmentation**: Supports augmentation for better model generalization.
- **Deep Learning Model**: Builds a convolutional neural network (CNN) for binary classification (Pneumonia vs. Normal).
- **Performance Metrics**: Evaluates the model using classification reports and confusion matrices.
## Dataset
The notebook uses the [Chest X-ray Pneumonia dataset](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia). Ensure you download and extract the dataset into the correct directory (`/kaggle/input/chest-xray-pneumonia/chest_xray`).
## Requirements
Install the following Python libraries to run the notebook:
- TensorFlow
- Keras
- NumPy
- Pandas
- Matplotlib
- Seaborn
- OpenCV
- Scikit-learn
- tqdm
## How to Run
1. Clone this repository:
```bash
git clone https://github.com/your-username/XRayNet.git
cd XRayNet
```
2. Ensure the dataset is in the correct directory.
3. Open the notebook in Jupyter or any compatible environment:
```bash
jupyter notebook xraynet-test-87.ipynb
```
4. Run the notebook cells sequentially to train and evaluate the model.
## Notebook Overview
### 1. **Imports and Setup**
- Necessary libraries for data loading, preprocessing, model creation, and evaluation.
### 2. **Data Preparation**
- Loads X-ray images from the dataset, preprocesses them by resizing and normalizing pixel values, and splits them into training and testing datasets, Note is Imbalance Data.
### 3. **Model Architecture**
- the main problem in the data was imbalanced, i try to solve is with many operation :
- Data Augmentation
- Class Weighting ( Most Accurate )
- Oversampling and Undersampling
- Transfer Learning with ResNet50
- Defines a CNN using TensorFlow/Keras with layers like Conv2D, MaxPooling, and BatchNormalization.
### 4. **Training**
- Trains the model using the training dataset with a specified batch size and learning rate.
### 5. **Evaluation**
- Generates predictions and evaluates the model using a confusion matrix and classification report.
## Results
Include a summary of model performance (accuracy, precision, recall, etc.) based on the output of the evaluation.
## Contributions
Feel free to submit pull requests or suggest new features. Feedback is always welcome!