---
language:
  - en
library_name: pytorch
license: mit
tags:
  - pneumonia
  - chest-xray
  - medical-imaging
  - cnn
  - image-classification
  - grad-cam
  - explainability
  - pytorch
  - custom-cnn
pipeline_tag: image-classification
metrics:
  - accuracy
  - f1
  - precision
  - recall
  - roc_auc
datasets:
  - chest-xray-pneumonia
framework:
  - PyTorch
  - TorchVision
---

# Pneumonia Classifier (Custom CNN)

A lightweight custom CNN for binary classification of chest X-rays (Normal vs Pneumonia) with Grad-CAM explainability.

## Model Details

- **Architecture**: Custom CNN (Net) — 9 Conv2d layers + Global Average Pooling
- **Input**: RGB chest X-ray images, resized to 224×224
- **Output**: Binary classification (Normal / Pneumonia) with softmax probabilities
- **Parameters**: ~35K (lightweight, edge-friendly)
- **Framework**: PyTorch

## Demo

Try the live demo: [Pneumonia AI Diagnostic Suite](https://pneumonia-classifie.streamlit.app/)

## Performance

| Metric | Score |
|--------|-------|
| Accuracy | 98% |
| Pneumonia Precision | 97% |
| Pneumonia Recall | 100% |
| Normal Precision | 100% |
| Normal Recall | 96% |
| Macro F1-Score | 0.98 |
| ROC AUC | 1.00 |

> Evaluated on 57 test samples with optimized threshold (0.1) for high sensitivity.

### Cross-Validation (5-Fold Stratified)

| Fold | Accuracy | F1-Score |
|------|----------|----------|
| 1 | 92.86% | 0.927 |
| 2 | 97.62% | 0.976 |
| 3 | 92.86% | 0.933 |
| 4 | 95.24% | 0.950 |
| 5 | 97.62% | 0.977 |
| **Mean** | **95.24%** | **0.953** |

## Training

- **Optimizer**: SGD with Momentum (0.8)
- **Loss**: Negative Log Likelihood (NLL) on log_softmax output
- **Epochs**: 18 (early stopping, patience=5)
- **Best Training Accuracy**: 98.25%
- **Data Augmentation**: Baseline (no augmentation) — the unaugmented model performed better (95.24% CV vs 92.38% with heavy augmentation)

## Quantization (INT8)

| | FP32 | INT8 | Improvement |
|--|------|------|-------------|
| Size | 243 KB | 52 KB | **4.64x** smaller |
| Latency | 108 ms | 27 ms | **3.99x** faster |

## Usage

```python
import torch
from huggingface_hub import hf_hub_download
from pneumonia_classifier.ml.model.arch import Net

# Download model
model_path = hf_hub_download(
    repo_id="24f2004275/pneumonia_classifier",
    filename="pneumonia_classifier_cnn_uza7heywpgthvahb.pt"
)

# Load model
model = Net()
model.load_state_dict(torch.load(model_path, map_location="cpu", weights_only=False))
model.eval()

# Inference
from torchvision import transforms

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# image = Image.open("chest_xray.jpg").convert("RGB")
# tensor = transform(image).unsqueeze(0)
# with torch.no_grad():
#     output = model(tensor)
#     probs = torch.exp(output)
#     prediction = "Pneumonia" if probs.argmax() == 1 else "Normal"
```

## Available Models

| File | Description |
|------|-------------|
| `pneumonia_classifier_cnn_uza7heywpgthvahb.pt` | Primary model (FP32, best accuracy) |
| `pneumonia_classifier_cnn_int8_tgzwzsqwqw54dahb.pt` | INT8 quantized variant |
| `pneumonia_classifier_cnn_int8_ra32tviwyo4u3ahb.pt` | INT8 quantized variant (alt) |
| `pneumonia_classifier_aug_baseline_no_augmentation_zvyot3qwqgnsfahb.pt` | Baseline (no augmentation) |
| `pneumonia_classifier_aug_augmented_heavy_wypxe3qwqsx75ahb.pt` | Heavy augmentation variant |

## Explainability

This model integrates **Grad-CAM** (Gradient-weighted Class Activation Mapping) to visualize which regions of the chest X-ray the model focuses on for its predictions. The heatmap highlights areas of radiographical density associated with pneumonia.

## License

MIT License

## Citation

```bibtex
@misc{pneumonia_classifier,
  title={Pneumonia Detection from Chest X-Rays using Custom CNN with Grad-CAM},
  author={Ayush Dubey},
  year={2024},
  url={https://huggingface.co/24f2004275/pneumonia_classifier}
}
```