---
license: apache-2.0
language:
- en
tags:
- medical-imaging
- kidney-disease
- ct-scan
- image-classification
- pytorch
- efficientnet
- custom-cnn
metrics:
- accuracy
- f1
library_name: pytorch
pipeline_tag: image-classification
datasets:
- nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone
---

# Kidney Ct Classifier Efficientnet

## Model Description

Custom EfficientNet-style CNN for kidney CT scan classification (101M params, 95%+ accuracy)

This is a custom EfficientNet-style CNN trained from scratch for kidney CT scan classification. The model classifies kidney CT images into 4 categories: Cyst, Normal, Stone, and Tumor.

## Model Details

- **Model Type:** Custom EfficientNet-style Convolutional Neural Network
- **Architecture:** 101M parameters, 7 stages with MBConv blocks
- **Input Resolution:** 384x384x3 RGB images
- **Number of Classes:** 4 (Cyst, Normal, Stone, Tumor)
- **Framework:** PyTorch 2.0+
- **Training Precision:** BF16 mixed precision on NVIDIA A100
- **No Pretrained Weights:** Trained from scratch on medical imaging data

## Performance

### Test Set Results

- **Accuracy:** 95.00%
- **F1-Score:** 0.9400

### Per-Class Performance

| Class | Precision | Recall | F1-Score |
|---|---|---|---|
| Cyst | High | High | High |
| Normal | High | High | High |
| Stone | Good | Good | Good |
| Tumor | Good | Good | Good |

## Training Details


The model was trained on the CT Kidney Dataset with the following approach:

- Custom EfficientNet-style architecture built from scratch
- 101 million trainable parameters
- Width multiplier: 1.4, Depth multiplier: 1.4
- Input resolution: 384x384 pixels
- BF16 mixed precision training on NVIDIA A100
- AdamW optimizer with OneCycleLR scheduler
- Extensive data augmentation (5x multiplication)
- No data leakage: splits created before augmentation
- Training time: 10.5 hours on A100 40GB


### Training Configuration

- **Epochs:** 40
- **Batch Size:** 48
- **Optimizer:** AdamW (lr=2e-3, weight_decay=2e-4)
- **Scheduler:** OneCycleLR with cosine annealing
- **Loss Function:** CrossEntropyLoss with label smoothing (0.1)
- **Regularization:** Dropout (0.35), Stochastic Depth (0-0.2)
- **Data Augmentation:** Extensive augmentation including rotation, flip, brightness/contrast, CLAHE
- **Hardware:** NVIDIA A100 40GB with BF16 precision
- **Training Time:** ~10.5 hours for 40 epochs

### Data Split

- **Training:** 80% (augmented to ~78,000 images)
- **Validation:** 10% (original, no augmentation)
- **Testing:** 10% (original, no augmentation)
- **No Data Leakage:** Splits created before augmentation

## Intended Use

### Primary Use Cases

- Medical research and educational purposes
- Kidney disease classification from CT scans
- Computer-aided diagnosis (CAD) system development
- Medical imaging research

### Limitations

- Model trained on specific dataset distribution
- Should not be used as sole diagnostic tool
- Requires clinical validation before medical use
- Performance may vary on images from different scanners or protocols

## How to Use

### Installation

```bash
pip install torch torchvision pillow
```
### Inference Example
```python
import torch
from PIL import Image
from torchvision import transforms

# Load model
model = torch.load('model.pth')
model.eval()

# Prepare image
transform = transforms.Compose([
    transforms.Resize((384, 384)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# Predict
image = Image.open('kidney_ct.jpg').convert('RGB')
image_tensor = transform(image).unsqueeze(0)

with torch.no_grad():
    output = model(image_tensor)
    probs = torch.softmax(output, dim=1)
    pred = output.argmax(1).item()

classes = ['Cyst', 'Normal', 'Stone', 'Tumor']
print(f"Prediction: {classes[pred]} ({probs[0][pred].item()*100:.1f}% confidence)")
```
## Dataset

**Name:** nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone

The model was trained on the CT Kidney Dataset containing 12,446 CT scan images across 4 classes. The dataset consists of coronal and axial cuts from PACS systems, verified by medical professionals.

## Ethical Considerations

- This model is for research and educational purposes only
- Not FDA approved or clinically validated
- Should not replace professional medical diagnosis
- Requires human oversight and clinical validation
- May have biases from training data distribution

## Citation

If you use this model in your research, please cite:
```bibtex
@misc{kidneyctclassifierefficientnet,
author = {Arko007},
title = {Kidney Ct Classifier Efficientnet},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{[https://huggingface.co/](https://huggingface.co/)Arko007/Kidney Ct Classifier Efficientnet}}
}
```
## License

Apache License 2.0 - See LICENSE file for details

## Contact

For questions or issues, please open an issue on the model repository.

## Acknowledgments

- Dataset: nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone
- Training infrastructure: NVIDIA A100 GPU
- Framework: PyTorch