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Arko007/
kidney-ct-classifier-efficientnet

nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone

apache-2.0

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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

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

Arko007/kidney-ct-classifier-efficientnet