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CleitonOERocha
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vision-transformer-glomerulus-classifier

Image Classification
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vision-transformer-glomerulus-classifier
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---
license: apache-2.0
tags:
- vision
- image-classification
- medical
- glomerulus
- pathology
- pytorch
- transformers
- vision-transformer
datasets:
- custom
metrics:
- accuracy
- f1
model-index:
- name: vision-transformer-glomerulus-classifier
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: Glomerulus Classification Dataset
      type: custom
    metrics:
    - type: accuracy
      value: 0.9491
    - type: f1
      value: 0.9493
---

# Vision Transformer for Glomerulus Classification

This model is a **Vision Transformer Large** fine-tuned for classification of renal glomerulus images, distinguishing between normal and crescentic conditions across different histological stains.

## Model Performance

- **Accuracy**: 0.9491 (94.91%)
- **F1-Score**: 0.9493 (94.93%)


## Supported Classes

The model classifies glomeruli into **10 classes**:

### Normal:
- `Normal_AZAN` - Normal glomerulus with Azan staining
- `Normal_HE` - Normal glomerulus with H&E staining
- `Normal_PAMS` - Normal glomerulus with PAMS staining
- `Normal_PAS` - Normal glomerulus with PAS staining
- `Normal_PICRO` - Normal glomerulus with Picro staining

### Crescentic:
- `Crescente_AZAN` - Crescentic glomerulus with Azan staining
- `Crescente_HE` - Crescentic glomerulus with H&E staining
- `Crescente_PAMS` - Crescentic glomerulus with PAMS staining
- `Crescente_PAS` - Crescentic glomerulus with PAS staining
- `Crescente_PICRO` - Crescentic glomerulus with Picro staining

## How to Use

```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "CleitonOERocha/vision-transformer-glomerulus-classifier"
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)

# Load and process image
image = Image.open("your_image.jpg")
inputs = processor(images=image, return_tensors="pt")

# Make prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class_id = predictions.argmax().item()
    predicted_class = model.config.id2label[predicted_class_id]
    confidence = predictions.max().item()

print(f"Predicted class: {predicted_class}")
print(f"Confidence: {confidence:.4f}")
```

## Technical Details

- **Architecture**: Vision Transformer Large (ViT-L/16)
- **Base Model**: google/vit-large-patch16-224-in21k
- **Input Size**: 224x224 pixels
- **Patch Size**: 16x16
- **Parameters**: ~300M
- **Framework**: PyTorch + Transformers

## Model Comparison

In our benchmark:
1. **Vision Transformer Large**: 94.91% (this model)
2. **Vision Transformer Base**: 93.53%
3. **ResNet-50**: 50.58%

## Performance by Class

| Class | Precision | Recall | F1-Score | Support |
|-------|-----------|--------|----------|---------|
| Crescente_AZAN | 0.9500 | 0.9344 | 0.9421 | 61 |
| Crescente_HE | 0.9665 | 0.9914 | 0.9788 | 233 |
| Crescente_PAMS | 0.9737 | 0.9487 | 0.9610 | 78 |
| Crescente_PAS | 0.9607 | 0.9661 | 0.9634 | 177 |
| Crescente_PICRO | 0.7179 | 0.6364 | 0.6747 | 44 |
| Normal_AZAN | 0.8714 | 0.9683 | 0.9173 | 63 |
| Normal_HE | 0.9908 | 0.9908 | 0.9908 | 434 |
| Normal_PAMS | 0.9785 | 0.9681 | 0.9733 | 94 |
| Normal_PAS | 0.9835 | 0.8151 | 0.8914 | 146 |
| Normal_PICRO | 0.6667 | 0.9565 | 0.7857 | 46 |

## Dataset

The model was trained on a custom dataset of renal glomerulus images with:
- **Total images**: 2,759 images
- **Training**: 1,383 images  
- **Test**: 1,376 images
- **Stains**: 5 types (AZAN, H&E, PAMS, PAS, PICRO)
- **Conditions**: Normal vs Crescentic


## Limitations

- Trained specifically for renal glomeruli
- Requires histological quality images
- Does not replace professional medical diagnosis
- Performance may vary with different equipment/protocols


---

**Note**: This model is for research and educational purposes. It should not be used as the sole source for medical diagnosis.