Oral Cancer Detection and Classification

Introduction

This project aims to develop a machine learning model for detecting and classifying oral cancer levels from images. It leverages a dataset sourced from Kaggle containing information relevant to oral cancer research.

Setup

1. Install Dependencies: Ensure you have the necessary dependencies installed. You can install them using the provided requirements.txt file:

   pip install -r requirements.txt
   

2. Kaggle API Credentials: Set up your Kaggle API credentials by replacing 'your_username' and 'your_api_key' in the Python code with your Kaggle username and API key, respectively.

Usage

1. Download Dataset: Run the provided Python script to download the dataset from Kaggle and extract it.

2. Data Exploration: Explore the dataset using the loaded Pandas DataFrame. Analyze basic statistics, visualize data distributions, and understand the structure of the dataset.

3. Model Development: Utilize machine learning techniques, such as convolutional neural networks (CNNs), to develop a model for oral cancer detection and classification.

File Structure

• oral_cancer_classification.ipynb: Jupyter Notebook containing the Python code for data loading, preprocessing, analysis, and model development.
• requirements.txt: File listing the required Python packages and their versions.
• dataset: Directory containing the downloaded dataset from Kaggle.
• README.md: This file providing an overview of the project.

Contributors

• UDAY G

License

This project is licensed under the MIT License.