Skin Detection System using Convolutional Neural Networks (CNN)

This repository contains the code for a skin detection system developed using Convolutional Neural Networks (CNN). The project is divided into two main parts: training the model and using a GUI to make predictions with the trained model.

Table of Contents

• Overview
• Features
• Prerequisites
• Installation
• Dataset
• Training the Model
• Running the GUI for Prediction
• Contributing
• License

Overview

This project is designed to detect skin conditions using image data. The system is built using a Convolutional Neural Network (CNN) that is trained on labeled skin image data. The trained model can then be used to make predictions on new images through a graphical user interface (GUI).

Features

• Training Script: A script to train the CNN model on a dataset of skin images and save the trained model.
• Prediction GUI: A GUI application that loads the saved model and allows users to make predictions on new images.
• Model Persistence: The trained model is saved to disk for reuse, eliminating the need to retrain the model every time the GUI is run.

Prerequisites

Before you can run the code, ensure you have the following software and libraries installed:

• Python 3.7+
• TensorFlow 2.5+
• Keras 2.5+
• NumPy
• OpenCV
• Tkinter (for the GUI)
• Matplotlib (for visualization)
• Scikit-learn

Installation

Follow these steps to set up your environment and run the code:

1. Clone the repository:

bash git clone https://github.com/your-username skin-detection-cnn.git cd skin-detection-cnn

2. Create a virtual environment (optional but recommended):

bash python -m venv venv source venv/bin/activate # On Windows use venv\Scripts\activate

3. Install the required packages:

bash pip install -r requirements.txt

4. Download and prepare the dataset:

• Download the dataset from Kaggle:
• Extract the dataset and place it in a directory named data/ within the project folder.
• The dataset should be structured as follows: bash

data/
├── train/
│   ├── benign/
│   ├── malignant/
│   └── ...
└── test/
    ├── benign/
    ├── malignant/
    └── ...

Note: Modify the dataset path in the code if your dataset structure is different.

Training the Model

To train the model, run the train.py script. This script will train the CNN on your dataset and save the trained model to a file.

bash python train.py

Customizing Training

You can adjust the training parameters such as batch size, number of epochs, learning rate, and others by editing the train.py script.

Running the GUI for Prediction

Once the model is trained and saved, you can use the GUI to make predictions on new images.

1. Run the GUI:

bash python gui_predict.py

2. Using the GUI:

• Load an image using the "Load Image" button.
• Click "Predict" to see the model’s prediction.
• The prediction result will be displayed on the GUI.

Contributing

Contributions are welcome! Please create an issue or submit a pull request for any features, enhancements, or bug fixes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.