README.md

metadata

title: SkinAI Diagnostics
emoji: 🔬
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 7860
pinned: false

Skin Cancer Classifier API

SkinAI Diagnostics — a FastAPI-powered web application for skin cancer image classification using deep learning.

---

Overview

This project delivers a professional web interface for classifying dermatoscopic images. It enables users to upload a skin lesion image and obtain a prediction with calibrated confidence scores, alongside links to technical details of the underlying model.

---

Repository Structure

skin-cancer-api/
├── app/
│   ├── main.py                # FastAPI application entry point
│   ├── predict.py             # Image preprocessing and prediction logic
│   ├── model_loader.py        # Loads the trained EfficientNetV2S model
│   ├── model/                 # Local model storage (auto-downloaded if missing)
│   │   └── efficientnetv2s.h5 # Pretrained model file
├── test_images/               # Sample images for testing the app
│    └── ...
├── requirements.txt           # Python dependencies
├── Dockerfile                 # Docker setup for deployment
├── .dockerignore              # Docker ignore rules
└── README.md                  # Project documentation (this file)

---

Features

• Seven-class classification of dermatoscopic images:
  • Actinic Keratoses and Intraepithelial Carcinoma (AKIEC)
  • Basal Cell Carcinoma (BCC)
  • Benign Keratosis-like Lesions (BKL)
  • Dermatofibroma (DF)
  • Melanoma (MEL)
  • Melanocytic Nevi (NV)
  • Vascular Lesions (VASC)
• Professional web interface built with FastAPI and Jinja2
• Temperature Scaling (T-scaling) for calibrated probabilities
• Technical transparency: model architecture, training setup, and metrics
• Confidence visualization with a probability chart
• Sample images available in test_images/

---

Model Details

• Base Model: EfficientNetV2S (20.5 million parameters)
• Dataset: HAM10000
• Calibration: Temperature Scaling (optimal T=2.77)
• Performance:
  • Accuracy: 0.88
  • Macro F1-score: 0.80
  • Expected Calibration Error (ECE): 0.022 (after T-scaling)

For full technical details, see the Model Technical Information section in the app.

---

Model Download from Hugging Face

The model file efficientnetv2s.h5 is hosted on Hugging Face Hub and is automatically downloaded the first time the app runs.

How it works:

• Expected local path: app/model/efficientnetv2s.h5
• On startup, app/model_loader.py checks for the file
• If missing, it is downloaded via huggingface_hub and saved to app/model/
• Subsequent runs load the local copy

Note for Docker users: When running inside a container, the downloaded model is stored inside the container’s filesystem. Mount a volume if you need to persist it on the host.

Manual download link (optional): https://huggingface.co/Miguel764/efficientnetv2s-skin-cancer-classifier

---

Installation & Usage

1) Clone the repository

git clone https://github.com/yourusername/skin-cancer-api.git
cd skin-cancer-api

2) Install dependencies

pip install -r requirements.txt

Note: Requires Python 3.8+ and TensorFlow 2.10.0 (GPU recommended).

3) Run the application

uvicorn app.main:app --reload

The app will be available at http://localhost:8000

Access the API docs at http://127.0.0.1:8000/docs

4) Try with sample images

Use the images in the test_images/ folder to test the classifier.

---

Docker Deployment

Build and run the app in a container:

docker build -t skin-cancer-api .
docker run -p 8000:8000 skin-cancer-api

---