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| license: mit |
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| # LungsAI - Pneumonia Detection Model |
|
|
| ## Model Description |
| LungsAI is a deep learning image classification model designed to analyze chest X-ray images and predict the presence of pneumonia. This AI-powered tool was developed to assist in fast and efficient medical image analysis. |
|
|
| - **Developed by:** Md Shahed Rahman |
| - **Model type:** Deep Learning Image Classification |
| - **Application:** Also deployed as a web application using Streamlit |
| - **License:** MIT |
|
|
| ## Model Details |
| The model weights are saved in the `.h5` format (`pneumonia_model.h5`), making it fully compatible with the Keras and TensorFlow frameworks. |
|
|
| ## How to Get Started with the Model |
| You can easily load and run predictions using this model in Python. Here is a basic code snippet to get you started: |
|
|
| ```python |
| import tensorflow as tf |
| from tensorflow.keras.models import load_model |
| from tensorflow.keras.preprocessing import image |
| import numpy as np |
| |
| # 1. Load the model |
| model = load_model('pneumonia_model.h5') |
| |
| # 2. Define a prediction function |
| def predict_pneumonia(img_path): |
| # Note: Adjust the target_size based on how your model was trained (e.g., 150x150 or 224x224) |
| img = image.load_img(img_path, target_size=(224, 224)) |
| img_array = image.img_to_array(img) |
| img_array = np.expand_dims(img_array, axis=0) / 255.0 |
| |
| # Get the prediction |
| prediction = model.predict(img_array) |
| return prediction |
| |
| # 3. Test with an image |
| # result = predict_pneumonia('path_to_your_xray_image.jpg') |
| # print(result) |
| |
| Intended Uses & Limitations |
| |
| Intended Use: This model is built for educational, research, and portfolio purposes to demonstrate the power of deep learning in healthcare. |
| |
| Limitations: This AI model is not a substitute for professional medical diagnosis. It should not be used for real-world clinical decision-making without the supervision of a certified doctor or radiologist. |
