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---
license: cc-by-nc-4.0
datasets:
- chest-xray-pneumonia
library_name: PyTorch
tags:
- pneumonia-detection
- cnn
- medical-imaging
- binary-classification
- chest-xray
- healthcare
- pytorch
model-index:
- name: ImprovedPneumoniaCNN
results:
- task:
type: image-classification
name: Pneumonia Detection
metrics:
- name: Accuracy
type: accuracy
value: 0.9676
- name: F1 Score
type: f1
value: 0.9685
- name: AUC
type: auc
value: 0.9959
- name: Loss
type: loss
value: 0.0778
---
# ImprovedPneumoniaCNN: Pneumonia Detection from Chest X-rays
This repository hosts `ImprovedPneumoniaCNN`, a custom Convolutional Neural Network model designed to detect **Pneumonia** from chest X-ray images. It incorporates enhancements like dropout, batch normalization, SiLU activation, and Convolutional Block Attention Module (CBAM) for improved robustness and generalization.
---
## Evaluation Results
| Metric | Score |
|----------|---------|
| Accuracy | 96.76% |
| F1 Score | 0.9685 |
| AUC | 0.9959 |
| Loss | 0.0778 |
---
### Confusion Matrix
| Normal| Pneumonia |
|-------|-----------|
| 1680 | 42 |
| 74 | 1782 |
---
### Classification Report
| Class | Precision | Recall | F1-Score | Support |
|-----------|-----------|--------|----------|---------|
| Normal | 0.96 | 0.98 | 0.97 | 1722 |
| Pneumonia | 0.98 | 0.96 | 0.97 | 1856 |
---
## 🏗️ Architecture Highlights
- Custom CNN with residual blocks
- Uses **CBAM** attention for spatial and channel refinement
- **SiLU** activation for better non-linearity
- **Dropout** and **BatchNorm** for regularization
- Final **Global Average Pooling** + FC layer
---
## How to Use
### Install Dependencies
```bash
pip install torch torchvision albumentations scikit-learn matplotlib seaborn
import torch
from torchvision import transforms
from PIL import Image
from model import ImprovedPneumoniaCNN # make sure model is defined/imported
# Load model
model = ImprovedPneumoniaCNN()
model.load_state_dict(torch.load("improved_pneumonia_cnn.pth", map_location=torch.device('cpu')))
model.eval()
# Preprocess image
transform = transforms.Compose([
transforms.Grayscale(),
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
img = Image.open("path_to_chest_xray.jpg")
img_tensor = transform(img).unsqueeze(0)
# Predict
with torch.no_grad():
output = model(img_tensor)
prediction = torch.sigmoid(output).item()
print("Pneumonia" if prediction > 0.5 else "Normal")
```
---
## Contributors
- [Thiyaga158](https://huggingface.co/Thiyaga158)
---
## License
This model is licensed under [CC BY-NC 3.0](https://creativecommons.org/licenses/by-nc/3.0/).
For research and educational use only.
--- |