Lung-Cancer-Detection-Using-Machine-Learning-Methods-Codes

Lung Cancer Detection Using Machine Learning Methods

Published in: IEEE Xplore

DOI: 10.1109/10487685

Paper: https://ieeexplore.ieee.org/document/10487685

Welcome to the official code repository for the paper: "Lung Cancer Detection Using Machine Learning Methods."

This repository provides resources, scripts, and models developed to create a comprehensive framework for lung cancer detection using advanced machine learning techniques.

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🔑 Key Features

• Advanced Algorithms: Implementation of Random Forest, SVM, KNN, Ridge Regression and GaussianNaive Bayes Models for lung cancer detection.
• Data Preprocessing: Techniques for data cleaning, normalization, and augmentation.
• Feature Engineering: Extraction of significant features from imaging datasets.
• Performance Metrics: Evaluation scripts for accuracy, sensitivity, and specificity.
• Visualization: Heatmaps, interpretability visualizations, and prediction outputs.

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📂 Repository Contents

• notebooks/: Jupyter notebooks for data analysis and model training.
• Core scripts for building and evaluating models.
• Tools for dataset preprocessing and augmentation.
• Metrics reports and visualizations.
• Detailed documentation.

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🚀 Getting Started

1. Clone this repository:

   git clone https://github.com/AnonXarkA/Lung-Cancer-Detection-Using-Machine-Learning-Methods-Codes.git

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🌟 Citation

If you use this repository in your research, please cite:

@inproceedings{10487685,
  author={Arka, Dipak Debnath and Tafhim, Sad Md. and Anan, Rawnak Muntaha and Rahat, Nusaibah and Ishan, Samiu Mostafa and Tanvir, Sifat},
  booktitle={2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)}, 
  title={Lung Cancer Detection Using Machine Learning Methods}, 
  year={2023},
  volume={},
  number={},
  pages={1-5},
  keywords={Computer science;Sensitivity;Lung cancer;Lung;Machine learning;Forestry;Feature extraction;Feature Encoding;Ridge Regression;KNN;SVM;Random Forest;Gaussian Naive Bayes},
  doi={10.1109/CSDE59766.2023.10487685}
}