Melanoma Skin Cancer
Melanoma Skin Cancer
Installation
To install the required packages for this project, use the following command after creating a virtual environment:
pip install -r requirements.txt
Note: The model was trained on Google Colab with GPU support.
About Project
This project aims to serve a deep learning model through an API using FastAPI.The Melanoma Skin Cancer Image dataset comprises 10,000 images, aimed at aiding in the development of accurate deep learning models for the classification of melanoma, a deadly form of skin cancer. With 9,600 images designated for training purposes and an additional 1,000 images reserved for model evaluation, the dataset presents a valuable resource for enhancing the early detection and treatment of melanoma, potentially saving numerous lives. Leveraging advanced machine learning techniques on this dataset can facilitate the creation of robust models capable of accurately identifying melanoma from skin images, thereby improving diagnostic accuracy and enabling timely intervention strategies.
How to Run the App
After installing the necessary packages, run the following command from the project root directory to start the app:
uvicorn app.main:app
Visit http://127.0.0.1:8000/docs from your browser to access Swagger. You can upload an image through the predict endpoint and receive a JSON response. Use the --reload argument to see immediate effects when changing code.
Alternative
Postman
Running Tests
To run the test cases, execute the following command from the project root directory:
pytest
How to Run the App with Docker
Ensure you are in the project root directory and Docker is running. Use the following command to create a Docker image:
docker build -t image-classifier-api .
Once the image is built successfully, run the container with the following commands:
docker run -p 8000:80 image-classifier-api
Visit http://127.0.0.1:8000/docs from your browser to access Swagger. You can upload an image through the predict endpoint and receive a JSON response.
Model Training and Performance
The model performs relatively well with an accuracy of 86%. However, it seems to perform better in identifying "benign" cases compared to "malignant" cases, as indicated by the higher precision and recall values for the "benign" class. Further analysis and fine-tuning of the model may be required to improve its performance, especially for the "malignant" class.
Project Link : Click Here
Project Created by Ravi Kumar M