Model card made

README.md

@@ -1,3 +1,118 @@
----
-license: gpl-3.0
----
+---
+license: gpl-3.0
+language:
+- en
+metrics:
+- accuracy
+tags:
+- medical
+- cancer
+- chemistry
+- biology
+- skin
+---
+
+# 🧠 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](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
+
+```python
+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**
+