---
title: Pneumonia Detection from X-ray Images
emoji: 🏥
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: "3.0"
python_version: "3.10"
suggested_hardware: "cpu-upgrade"
app_file: "./app.py"
fullWidth: true
header: mini
short_description: " model to detect pneumonia from chest X-ray images."
tags:
- deep-learning
- medical-imaging
- computer-vision
- pneumonia-detection
thumbnail: "URL_to_thumbnail_image"
---

## Model Description
This model employs a MobileNetV3 architecture fine-tuned for the detection of pneumonia from chest X-ray images. It is designed to assist radiologists by providing a preliminary automated diagnosis.
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## Training Data
The model was trained on the [Kaggle Pneumonia dataset](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia), which contains thousands of labeled chest X-ray images from children.

## Model Architecture
The model uses MobileNetV3 as the base for feature extraction, with additional custom layers to tailor it for pneumonia detection.

## Training Procedure
The model was trained with an Adam optimizer and early stopping based on validation loss to prevent overfitting. Data augmentation techniques such as rotations and flips were used to enhance generalization.

## Performance
The model achieved a high accuracy on the validation set, with the following metrics:
- Accuracy: XX%
- Precision: XX%
- Recall: XX%
- F1 Score: XX%

## Usage
Here is an example of how to use this model:

```python
import gradio as gr
import tensorflow as tf

model = tf.keras.models.load_model('model.h5')

def predict(image):
    processed_image = preprocess_image(image)
    return model.predict(processed_image)

iface = gr.Interface(fn=predict, inputs="image", outputs="label")
iface.launch()