| --- |
| license: mit |
| language: |
| - en |
| library_name: pytorch |
| pipeline_tag: image-classification |
| datasets: |
| - nsr51324/Oral_Diseases |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| base_model: |
| - resnet50 |
| tags: |
| - image-classification |
| - computer-vision |
| - medical-imaging |
| - dentistry |
| - oral-health |
| - resnet50 |
| - transfer-learning |
| - pytorch |
| - deep-learning |
| --- |
| |
| # 🦷 Oral Diseases Image Classification |
|
|
| A **ResNet50-based deep learning model** fine-tuned to classify **six common oral diseases** from intraoral images. This repository contains the best-performing model from a benchmark of four convolutional neural network architectures trained and evaluated under identical conditions. |
|
|
| 🏆 **Best Model:** ResNet50 |
|
|
| ✅ **Accuracy:** **94.77%** |
|
|
| 🎯 **Macro F1-Score:** **0.9411** |
|
|
| 🧠 **Framework:** PyTorch |
|
|
| --- |
|
|
| # Model Overview |
|
|
| The model classifies the following six oral conditions: |
|
|
| - Calculus |
| - Caries |
| - Gingivitis |
| - Ulcers |
| - Tooth Discoloration |
| - Hypodontia |
|
|
| The final model was obtained using **transfer learning** with an ImageNet-pretrained ResNet50 and fine-tuned using a two-stage training strategy. |
|
|
| --- |
|
|
| # Benchmark Results |
|
|
| | Rank | Model | Trainable Parameters | Accuracy | Macro F1 | |
| |------|--------|--------------------:|---------:|----------:| |
| | 🥇 | ResNet50 | 23,520,326 | **94.77%** | **0.9411** | |
| | 🥈 | DenseNet121 | 6,960,006 | 94.51% | 0.9351 | |
| | 🥉 | EfficientNet-B0 | 4,015,234 | 94.17% | 0.9335 | |
| | 4 | Scratch CNN | 11,179,590 | 83.45% | 0.8236 | |
|
|
| --- |
|
|
| # Repository Structure |
|
|
| ``` |
| checkpoints/ |
| │── best_model.pth |
| |
| notebooks/ |
| │── oral-disseases-image-classification.ipynb |
| |
| outputs/ |
| │── models_comparison.csv |
| │── resnet50_confusion_matrix.png |
| │── resnet50_history.png |
| │── densenet121_confusion_matrix.png |
| │── densenet121_history.png |
| │── efficientnet_b0_confusion_matrix.png |
| │── efficientnet_b0_history.png |
| │── scratch_cnn_confusion_matrix.png |
| │── scratch_cnn_history.png |
| |
| Gradio.py |
| README.md |
| ``` |
|
|
| --- |
|
|
| # Download |
|
|
| ## Model Weights |
|
|
| The trained checkpoint is available in: |
|
|
| ``` |
| checkpoints/best_model.pth |
| ``` |
|
|
| or can be downloaded directly from this repository. |
|
|
| --- |
|
|
| ## Dataset |
|
|
| Training dataset: |
|
|
| https://huggingface.co/datasets/nsr51324/Oral_Diseases |
| |
| Original source: |
| |
| Oral Diseases Dataset (Kaggle) |
| |
| --- |
| |
| # How to Load the Model |
| |
| ```python |
| from huggingface_hub import hf_hub_download |
| import torch |
|
|
| weights_path = hf_hub_download( |
| repo_id="nsr51324/Oral_Diseases_Image_Classification", |
| filename="checkpoints/best_model.pth" |
| ) |
|
|
| checkpoint = torch.load(weights_path, map_location="cpu") |
| class_names = checkpoint["class_names"] |
| ``` |
| |
| --- |
| |
| # Inference |
| |
| ```python |
| import torch |
| import torch.nn as nn |
| from torchvision.models import resnet50 |
| from torchvision import transforms |
| from PIL import Image |
|
|
| model = resnet50(weights=None) |
|
|
| model.fc = nn.Sequential( |
| nn.Dropout(0.3), |
| nn.Linear(model.fc.in_features, len(class_names)) |
| ) |
| |
| model.load_state_dict(checkpoint["state_dict"]) |
| model.eval() |
| |
| transform = transforms.Compose([ |
| transforms.Resize((224,224)), |
| transforms.ToTensor(), |
| transforms.Normalize( |
| [0.485,0.456,0.406], |
| [0.229,0.224,0.225] |
| ) |
| ]) |
| |
| image = Image.open("sample.jpg").convert("RGB") |
| tensor = transform(image).unsqueeze(0) |
| |
| with torch.no_grad(): |
| probabilities = torch.softmax(model(tensor), dim=1)[0] |
| |
| prediction = class_names[probabilities.argmax().item()] |
| |
| print(prediction) |
| ``` |
| |
| --- |
| |
| # Interactive Demo |
| |
| A standalone Gradio application is included. |
| |
| Run: |
| |
| ```bash |
| pip install torch torchvision gradio pillow huggingface_hub |
|
|
| python Gradio.py |
| ``` |
| |
| --- |
| |
| # Training Details |
| |
| | Item | Value | |
| |------|-------| |
| | Image Size | 224 × 224 | |
| | Batch Size | 32 | |
| | Epochs | Up to 30 | |
| | Optimizer | Adam | |
| | Early Stopping | Yes | |
| | Weight Decay | 1e-4 | |
| | Label Smoothing | 0.1 | |
| | Dropout | 0.4 | |
| |
| Training consisted of two stages: |
| |
| 1. Freeze the ResNet50 backbone and train the classifier head. |
| 2. Unfreeze the backbone and fine-tune the entire network. |
| |
| --- |
| |
| # Data Augmentation |
| |
| The following augmentations were applied during training: |
| |
| - Random Resized Crop |
| - Horizontal Flip |
| - Rotation |
| - Color Jitter |
| - Random Erasing |
| |
| --- |
| |
| # Evaluation |
| |
| The repository includes: |
| |
| - Confusion matrices |
| - Training history |
| - Classification metrics |
| - Model comparison |
| - CSV benchmark results |
| |
| See the **outputs/** directory for complete evaluation results. |
| |
| --- |
| |
| # Intended Use |
| |
| This model is intended for **research, educational purposes, and AI experimentation**. |
| |
| It is **not** a certified medical device and **must not** be used as a substitute for professional clinical diagnosis. |
| |
| --- |
| |
| # License |
| |
| This project is released under the **MIT License**. |
| |
| Please refer to the dataset license before commercial use. |
| |
| --- |
| |
| # Author |
| |
| **Nasr Mohamed** |
| |
| AI Engineer |
| |
| 🤗 https://huggingface.co/nsr51324 |
