SpotCheck: Early-Stage Skin Cancer Detection

Welcome to the SpotCheck project repository. SpotCheck is a web application designed for early-stage skin cancer detection with high accuracy. Developed using Flask and TensorFlow, this project was a part of the HackMIT competition, where it secured second place.

Table of Contents

• Introduction
• Project Objectives
• Technology and Tools
• Setup
• Usage
  • Running the Application
  • Uploading an Image
• File Structure
• License
• Contact

Introduction

SpotCheck is a web application that detects early-stage skin cancer using machine learning algorithms. It leverages TensorFlow for model training and Flask for the web interface, achieving an impressive accuracy rate of 98%.

Project Objectives

1. Detect early-stage skin cancer with high accuracy.
2. Provide a user-friendly web interface for uploading images and viewing results.
3. Ensure the application is accessible for early-stage skin cancer detection.

Technology and Tools

• Framework: Flask
• Machine Learning: TensorFlow
• Languages: Python
• Other Tools: Numpy, Keras, HTML, CSS

Setup

1. Clone the repository:

   git clone https://github.com/Sreechandh22/SPOTCHECK.git
   cd SpotCheck

2. Create a virtual environment and activate it:

   python3 -m venv venv
   source venv/bin/activate

3. Install the required packages:

   pip install -r requirements.txt

4. Ensure the pre-trained model is in the correct location:

   Place skin_cancer_model_v2.keras in the root directory of the project.

Usage

Running the Application

1. Start the Flask server:

   python app.py

2. Open your web browser and navigate to http://127.0.0.1:5000/

Uploading an Image

1. Upload an image: Choose a .jpg or .png image file of a skin lesion.
2. Submit the image: The app will process the image and provide a risk percentage for skin cancer.

File Structure

SpotCheck/
├── Templates/
│   ├── index.html
│   ├── result.html
├── code/
│   ├── Anomaly_Detection.py
│   ├── Code_Generation.py
│   ├── Data_Visualization.py
│   ├── GAN_model.py
│   ├── GPT_Analysis.py
│   ├── User_Interaction.py
│   ├── Visual_Predict.py
│   ├── app.py
│   ├── convert_to_tflite.py
│   ├── hack1.py
│   ├── hack2.py
│   ├── kaggle.json
├── skin_cancer_model_v2.keras
├── .gitignore
├── LICENSE
├── README.md
└── requirements.txt

• Templates/: HTML templates for rendering web pages.
• code/: Contains various Python scripts for different functionalities.
  • app.py: Main application file for running the Flask server.
  • convert_to_tflite.py: Converts the model to TensorFlow Lite format.
  • Other scripts: Supportive scripts for data analysis and visualization.
• skin_cancer_model_v2.keras: The pre-trained model for skin cancer detection.
• .gitignore: Specifies files and directories to be ignored by Git.
• LICENSE: The project’s license information.
• README.md: Project overview and setup guide.
• requirements.txt: List of dependencies required to run the project.

License

This project is licensed under the MIT License.

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Contact

For any inquiries or collaboration opportunities, please contact sreechandh2204@gmail.com