🧠 Model Card for Skin Cancer ResNet18 Classifier

This is a binary skin lesion classifier based on ResNet-18. It was trained to distinguish between benign and malignant dermoscopic images using the HAM10000 dataset. The model is part of a privacy-focused research project for on-device melanoma risk screening.

🧬 Model Details

📝 Model Description

👨‍💻 Developed by: Conn Finnegan
🧠 Model type: Image classifier (ResNet18)
🔧 Finetuned from model: torchvision.models.resnet18(pretrained=True)
🖼️ Input shape: RGB image (3 x 224 x 224)
🏷️ Output classes:
- Class 0: Benign
- Class 1: Malignant

📦 Model Sources

📁 Repository: https://huggingface.co/connfinnegan/skin-cancer-resnet18
🧪 Demo: Coming soon via Hugging Face Spaces

🚀 Uses

🎯 Direct Use

Used for inference on dermoscopic mole/lesion images to estimate if a lesion is likely benign or malignant.

🚫 Out-of-Scope Use

Not intended as a diagnostic medical tool.
Not trained on diverse skin tones or photographic image types.

⚠️ Bias, Risks, and Limitations

Model trained on dermoscopic images from the HAM10000 dataset, which is not representative of all skin types or lesion types.
False negatives (missed malignancies) could be harmful.
False positives may cause unnecessary concern.

✅ Recommendations

Always consult a healthcare professional. This model is a research prototype only.

🧪 How to Get Started with the Model

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

model = models.resnet18()
model.fc = torch.nn.Linear(model.fc.in_features, 2)
model.load_state_dict(torch.load("skin_cancer_resnet18_v1.pt", map_location='cpu'))
model.eval()

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor()
])

img = Image.open("your_image.jpg").convert("RGB")
input_tensor = transform(img).unsqueeze(0)

with torch.no_grad():
    output = model(input_tensor)
    pred = torch.argmax(output, dim=1).item()
    print("Prediction:", "benign" if pred == 0 else "malignant")

🏋️ Training Details

🗂️ Training Data

Dataset: HAM10000 (Kaggle)
Malignant classes grouped: melanoma, bcc, akiec
Benign classes grouped: nv, bkl, df, vasc

⚙️ Training Procedure

📏 Input resolution: 224x224
🧠 Optimiser: Adam
📉 Loss function: Weighted Cross Entropy
🔁 Epochs: 50 with early stopping
⚖️ Class weights: applied (malignant overweighted ~3.5x)
🧱 Framework: PyTorch 2.0.0

📈 Evaluation

✅ Accuracy: ~89%
📊 Malignant recall: ~78%
🎯 Benign precision: >90%

🧰 Technical Specifications

🏗️ Architecture: ResNet18 (modified last FC layer)
🧪 Framework: PyTorch + Torchvision
🐍 Python version: 3.10
📦 Dependencies: torchvision, torch, PIL, numpy

📬 Model Card Contact

👤 Author: Conn Finnegan
🔗 LinkedIn: https://www.linkedin.com/in/conn-finnegan-09a98124b/
🔗 GitHub: https://github.com/Conn-Finnegan