Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development, ICSETPSD 2023, 17th-18th November 2023, Coimbatore, Tamilnadu, India

Research Article

Employing U-NET and RBCNN to Build an Automatic Lung Cancer Segmentation and Classification System

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  • @INPROCEEDINGS{10.4108/eai.17-11-2023.2342832,
        author={P  Dhivya and P  Yamini},
        title={Employing U-NET and RBCNN to Build an Automatic Lung Cancer Segmentation and Classification System},
        proceedings={Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development, ICSETPSD 2023, 17th-18th November 2023, Coimbatore, Tamilnadu, India},
        publisher={EAI},
        proceedings_a={ICSETPSD},
        year={2024},
        month={1},
        keywords={computer assisted system lung cancer detection lung nodule segmentation feature extraction rbcnn classification},
        doi={10.4108/eai.17-11-2023.2342832}
    }
    
  • P Dhivya
    P Yamini
    Year: 2024
    Employing U-NET and RBCNN to Build an Automatic Lung Cancer Segmentation and Classification System
    ICSETPSD
    EAI
    DOI: 10.4108/eai.17-11-2023.2342832
P Dhivya1,*, P Yamini1
  • 1: IFET College of Engineering
*Contact email: divi.diviner@gmail.com

Abstract

The most common cancer-related cause of death globally is lung cancer. The key to effective lung cancer treatment and higher survival rates is early diagnosis. Converting a radiologist's diagnosing procedure to computer assisted results in more accurate results and an earlier diagnosis. The difficulty is that building a effective model for segmentation and classification. In this paper, we suggest a system for detecting lung cancer that makes use of a number of methods for precise and effective diagnosis. To enhance picture quality, our method pre-processes CT scan images using a Gaussian filter and contrast stretching. For the purpose of determining the borders of lung nodules with high precision, the U-Net architecture with the Adam optimizer is used. Then, a Gaussian mixture model (GMM) with EM optimisation and pixel padding is used to extract features. The rotational-based CNN (RBCNN) classifier successfully categorises the nodules as benign and malignant using these form variables as inputs