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license: gpl-3.0
language:
- en
metrics:
- accuracy
- sensitivity
- specificity
base_model: google/mobilenet-v2
new_version: "true"
pipeline_tag: image-classification
library_name: pytorch
tags:
- medical
- oral-cancer
- healthcare
- mobileNet
- image-classification
- pytorch
eval_results:
accuracy: 0.95
sensitivity: 0.93
specificity: 0.91
---
# Umlomo – Oral Cancer Detection Model
This model is a fine‑tuned **MobileNetV2** for binary classification of oral cavity images into **Normal** or **Oral Cancer**. It is part of the MySmile project, an AI‑powered oral health screening tool designed to empower individuals with early risk assessment.
## Model Details
- **Base Architecture:** MobileNetV2 (pretrained on ImageNet)
- **Fine‑tuned Dataset:** Curated oral images (normal and cancerous)
- **Input Size:** 224×224 RGB
- **Output:** Two classes – `Normal` and `Oral Cancer`
- **Framework:** PyTorch
## Intended Use
This model is intended for research and educational purposes within the MySmile screening application. It provides a preliminary risk assessment and is **not a substitute for professional medical diagnosis**.
## How to Use
### Installation
```bash
pip install torch torchvision pillow
import torch
from torchvision import transforms
from PIL import Image
# Load model
model = torch.hub.load('mysmile/umlomo', 'model', trust_repo=True)
model.eval()
# Preprocess image
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])
])
image = Image.open('oral_photo.jpg').convert('RGB')
input_tensor = transform(image).unsqueeze(0)
# Inference
with torch.no_grad():
outputs = model(input_tensor)
probs = torch.softmax(outputs, dim=1)
pred_idx = torch.argmax(probs, dim=1).item()
class_names = ['Normal', 'Oral Cancer']
print(f"Prediction: {class_names[pred_idx]}, Confidence: {probs[0][pred_idx]:.2f}")
```
--- |