| --- |
| license: mit |
| tags: |
| - image-classification |
| - pytorch |
| - resnet |
| - medical |
| - dental |
| - orthodontics |
| datasets: |
| - custom |
| metrics: |
| - accuracy |
| pipeline_tag: image-classification |
| --- |
| |
| # Orthodontic Condition Classifier |
|
|
| A ResNet18-based image classification model trained to detect orthodontic conditions from dental photos. |
|
|
| ## Model Details |
|
|
| - **Architecture**: ResNet18 |
| - **Input Size**: 512x512 RGB images |
| - **Output**: 8 orthodontic condition classes |
| - **Test Accuracy**: 72.73% |
|
|
| ## Classes |
|
|
| 1. Crossbite |
| 2. Crowding |
| 3. Deepbite |
| 4. No Treatment Needed |
| 5. Open Bite |
| 6. Overbite |
| 7. Spacing |
| 8. Underbite |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from torchvision import transforms, models |
| from PIL import Image |
| |
| # Load model |
| model = models.resnet18(weights=None) |
| model.fc = torch.nn.Linear(model.fc.in_features, 8) |
| state_dict = torch.load("pytorch_model.pth", map_location="cpu") |
| model.load_state_dict(state_dict) |
| model.eval() |
| |
| # Preprocess image |
| transform = transforms.Compose([ |
| transforms.Resize((512, 512)), |
| transforms.ToTensor(), |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) |
| ]) |
| |
| image = Image.open("dental_photo.jpg").convert("RGB") |
| input_tensor = transform(image).unsqueeze(0) |
| |
| # Predict |
| with torch.no_grad(): |
| outputs = model(input_tensor) |
| probabilities = torch.nn.functional.softmax(outputs, dim=1) |
| predicted_class = torch.argmax(probabilities, dim=1).item() |
| ``` |
|
|
| ## Training Data |
|
|
| Trained on a custom dataset of dental photographs labeled by orthodontic condition. |
|
|
| ## Limitations |
|
|
| - This model is for screening purposes only and should not replace professional orthodontic evaluation |
| - Accuracy may vary based on image quality and lighting conditions |
| - Best results with clear, well-lit frontal photos of teeth |
|
|
| ## License |
|
|
| MIT License |
|
|
