derm-cnn-ham10000

derm-cnn-ham10000

A convolutional neural network trained on the HAM10000 dataset for multi-class skin lesion classification.

This model predicts 7 skin lesion categories from dermatoscopic images.
It is lightweight, easy to deploy, and comes with an inference script for quick testing.

---

Model Details

Architecture: Custom CNN (4 conv blocks + 5 fully-connected layers)
Input: RGB image resized to 28×28
Output: 7-class logits
Framework: PyTorch
Weights: model.pth

Classes

labels.json contains this mapping.

---

Performance

Metrics computed on the official HAM10000 split:

• Accuracy: 0.99
• Macro F1-score: 0.99
• Weighted F1-score: 0.99

Class-level summary:

Full report available in classification_report.txt.

---

How to Use

Install dependencies

pip install torch torchvision numpy pillow

Load the model

import torch
from model import load_model
from inference import predict

pred_idx, label, probs = predict("example.jpg", "model.pth")
print(label)

CLI usage

python inference.py path/to/image.jpg --weights model.pth

---

Repository Structure

model.py                # CNN architecture + load_model()
inference.py            # Run prediction on an input image
model.pth               # Trained weights
labels.json             # Class index → label
classification_report.txt
assets/                 # Confusion matrix, training curves (optional)

---

Training Data

Dataset: HAM10000
License: CC BY-NC 4.0

Preprocessing:

• resize to 28×28
• normalized to [0,1]
• no dataset augmentation used in the published version

---

Limitations

• The model is trained only on HAM10000 at 28×28 resolution.
• Predictions must not be used for medical diagnosis.
• HAM10000 includes class imbalance, which may affect edge cases.
• Performance on clinical camera photos is not guaranteed.

---

License

Model weights: CC BY-NC 4.0 (non-commercial use only)
Code: MIT License

This restriction comes from the HAM10000 dataset license.

---

Citation

If you use this model, please cite the HAM10000 dataset:

Tschandl, P., Rosendahl, C., & Kittler, H. (2018). 
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. 
Scientific Data, 5, 180161.