Skip to content

README.md

Latest commit

 

History

History
32 lines (22 loc) · 1.63 KB

README.md

File metadata and controls

32 lines (22 loc) · 1.63 KB
Raw

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

License

This project is licensed under the MIT License.