About The Project

Any technologies that can give more efficient, or faster analysis are very valuable. In recent decades, scientist and software developers are working together to improve healthcare technologies. Artificial Intelligence is playing huge role in these studies. Many studies have already surpassed human achievement, and many doctors use these systems as a decision mechanism, or diagnose the disease. Especially in cancer diagnosis or early diagnosis, artificial intelligence models provide great benefits to doctors in terms of speed and accuracy. Therefore, project objective is to develop machine learning models that can classify correctly skin lessions. This project will be carried out using machine learning methods and will be evaluated using common model evaluation techniques.

Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

• Python

• Jupyter Notebook Link

Installation

1. Clone the repo

   git clone https://github.com/muzaffersenkal/ML_Project

2. Go to Project Folder

   cd ML_Project

3. Install Requirements File

   pip install -r requirements.txt

4. Download Datasets

   Download

5. Start Jupyter Notebook

   jupyter notebook

Folder Structure

After you enter the second line of code, you'll see a series of automated messages as ProjectTemplate goes about doing its work. This work involves:

• Reports files are in the reports directory.
• Analysis are in the src directory.
• Datasets path data
• Figures and graphs are in graphs directory.
• Logs are in logs directory.

Demo Page / User Interface

1. Download model weights. Link

2. Define your model path in env variables.

   vim app/.env
   
   MODEL_PATH=your_model_path

3. Run Demo App

   streamlit run app/main.py

Contributing

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Contact

Muzaffer Senkal - email

Project Link: https://github.com/muzaffersenkal/ML_Project