---
license: mit
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: image-classification
tags:
- medical
- cervical-cancer
- multi-class
- ood-detection
---

# Model Card: DenseNet121 for Cervix type Image Classification

This model classifies cervical images into **Type_1, Type_2, Type_3**, and an **Out-of-Distribution (OOD)** category. It uses a **DenseNet121 backbone** pretrained on ImageNet and fine-tuned on cervical images, including OOD examples from Caltech101.  


### Model Details

- **Base model:** `torchvision.models.densenet121` pretrained on ImageNet  
- **Input:** RGB images (224x224)  
- **Output:** 4 classes: `['Type_1', 'Type_2', 'Type_3', 'OOD']`  
- **License:** MIT  
- **Training dataset sources:**  
  - Cervical images: Intel MobileODT competition dataset  
  - OOD images: Caltech101 dataset  
- **Preprocessing & Augmentation:**  
  - Resize to 224x224  
  - Normalization (ImageNet mean & std)  
  - Data augmentation: Random rotation, color jitter (brightness/contrast)  

### Dataset Distribution

| Split      | Type_1 | Type_2 | Type_3 | OOD  | Total |
| ---------- | ------ | ------ | ------ | ---- | ----- |
| Train      | 557    | 532    | 547    | 424  | 2060  |
| Validation | 151    | 161    | 154    | 122  | 588   |
| Test       | 73     | 88     | 80     | 54   | 295   |

### Training Details

- Optimizer: Adam  
- Loss: CrossEntropyLoss  
- Batch size: 8  
- Learning rate: 1e-5  
- Epochs: 30  
- Device: GPU (Tesla T4, 14GB)

## Evaluation

### Evaluation Metrics

| Class   | Precision | Recall | F1-score | Sensitivity | Specificity |
|---------|----------|--------|----------|-------------|-------------|
| OOD     | 1.00     | 1.00   | 1.00     | 1.0000      | 1.0000      |
| Type_1  | 0.74     | 0.93   | 0.82     | 0.9333      | 0.9074      |
| Type_2  | 0.85     | 0.51   | 0.64     | 0.5114      | 0.9574      |
| Type_3  | 0.73     | 0.92   | 0.81     | 0.9189      | 0.8762      |

**Overall accuracy:** 0.81  

**Confusion Matrix**
```
       Predicted
        OOD  T1  T2  T3
Actual
OOD      54   0   0   0
Type_1    0  56   3   1
Type_2    0  19  45  24
Type_3    0   1   5  68

```

**Classification Report**

```
              precision    recall  f1-score   support
OOD           1.00      1.00      1.00        54
Type_1        0.74      0.93      0.82        60
Type_2        0.85      0.51      0.64        88
Type_3        0.73      0.92      0.81        74

accuracy                           0.81       276
macro avg       0.83      0.84      0.82       276
weighted avg    0.82      0.81      0.80       276

```

---

## How to Get Started

``````python
import torch
from torchvision import transforms, models
from PIL import Image

# Load model
model = models.densenet121(pretrained=False)
model.classifier = torch.nn.Linear(model.classifier.in_features, 4)
model.load_state_dict(torch.load("Dense_net_121.pth", map_location="cpu"))
model.eval()

# Transform
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])
])

# Load image
image = Image.open("example.jpg").convert("RGB")
image = transform(image).unsqueeze(0)

# Predict
outputs = model(image)
probabilities = torch.softmax(outputs, dim=1)
predicted_class = torch.argmax(probabilities, dim=1).item()
confidence = probabilities[0, predicted_class].item()

class_names = ["Type_1", "Type_2", "Type_3", "OOD"]
print(f"Predicted class: {class_names[predicted_class]}, confidence: {confidence:.2f}")

````

---
## Technical Specifications

### Model Architecture

* **Backbone:** DenseNet121 pretrained on ImageNet
* **Output Layer:** Fully connected layer with 4 outputs (`Type_1`, `Type_2`, `Type_3`, `OOD`)
* **Activation:** Softmax for multi-class classification
* **Training Framework:** PyTorch
* **Loss Function:** CrossEntropyLoss
* **Data Handling:** Includes OOD images from Caltech101 along with in-distribution cervical images
* **Preprocessing & Augmentation:** Resize to 224x224, normalization (ImageNet mean/std), random rotation, color jitter

### Compute Infrastructure

* **Hardware:** Tesla T4 GPU (14GB)
* **Software:** PyTorch, torchvision, CUDA

---