Improved graph neural network-based green anaconda optimization for segmenting and classifying the lung cancer

S. Dinesh Krishnan; Danilo Pelusi; A. Daniel; V. Suresh; Balamurugan Balusamy; S. Dinesh Krishnan; Danilo Pelusi; A. Daniel; V. Suresh; Balamurugan Balusamy

doi:10.3934/mbe.2023764

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 17138-17157. doi: 10.3934/mbe.2023764

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Improved graph neural network-based green anaconda optimization for segmenting and classifying the lung cancer

1.
Assistant professor, B V Raju Institute of Technology, Narsapur, Telangana, India
2.
Department of Communication Sciences, University of Teramo, Italy
3.
Associate Professor, Amity University, Gwalior, Madhya Pradesh, India
4.
Assistant professor, Dr. N. G. P Institute of Technology, Coimbatore, India
5.
Associate Dean-Student Engagement, Shiv Nadar University, India

Academic Editor: Yang Kuang

Received: 10 July 2023 Revised: 12 August 2023 Accepted: 21 August 2023 Published: 04 September 2023

Normal lung cells incur genetic damage over time, which causes unchecked cell growth and ultimately leads to lung cancer. Nearly 85% of lung cancer cases are caused by smoking, but there exists factual evidence that beta-carotene supplements and arsenic in water may raise the risk of developing the illness. Asbestos, polycyclic aromatic hydrocarbons, arsenic, radon gas, nickel, chromium and hereditary factors represent various lung cancer-causing agents. Therefore, deep learning approaches are employed to quicken the crucial procedure of diagnosing lung cancer. The effectiveness of these methods has increased when used to examine cancer histopathology slides. Initially, the data is gathered from the standard benchmark dataset. Further, the pre-processing of the collected images is accomplished using the Gabor filter method. The segmentation of these pre-processed images is done through the modified expectation maximization (MEM) algorithm method. Next, using the histogram of oriented gradient (HOG) scheme, the features are extracted from these segmented images. Finally, the classification of lung cancer is performed by the improved graph neural network (IGNN), where the parameter optimization of graph neural network (GNN) is done by the green anaconda optimization (GAO) algorithm in order to derive the accuracy maximization as the major objective function. This IGNN classifies lung cancer into normal, adeno carcinoma and squamous cell carcinoma as the final output. On comparison with existing methods with respect to distinct performance measures, the simulation findings reveal the betterment of the introduced method.
- lung cancer segmentation,
- lung cancer classification,
- Improved Graph Neural Network,
- Green Anaconda Optimization
Citation: S. Dinesh Krishnan, Danilo Pelusi, A. Daniel, V. Suresh, Balamurugan Balusamy. Improved graph neural network-based green anaconda optimization for segmenting and classifying the lung cancer[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 17138-17157. doi: 10.3934/mbe.2023764

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Abstract

Normal lung cells incur genetic damage over time, which causes unchecked cell growth and ultimately leads to lung cancer. Nearly 85% of lung cancer cases are caused by smoking, but there exists factual evidence that beta-carotene supplements and arsenic in water may raise the risk of developing the illness. Asbestos, polycyclic aromatic hydrocarbons, arsenic, radon gas, nickel, chromium and hereditary factors represent various lung cancer-causing agents. Therefore, deep learning approaches are employed to quicken the crucial procedure of diagnosing lung cancer. The effectiveness of these methods has increased when used to examine cancer histopathology slides. Initially, the data is gathered from the standard benchmark dataset. Further, the pre-processing of the collected images is accomplished using the Gabor filter method. The segmentation of these pre-processed images is done through the modified expectation maximization (MEM) algorithm method. Next, using the histogram of oriented gradient (HOG) scheme, the features are extracted from these segmented images. Finally, the classification of lung cancer is performed by the improved graph neural network (IGNN), where the parameter optimization of graph neural network (GNN) is done by the green anaconda optimization (GAO) algorithm in order to derive the accuracy maximization as the major objective function. This IGNN classifies lung cancer into normal, adeno carcinoma and squamous cell carcinoma as the final output. On comparison with existing methods with respect to distinct performance measures, the simulation findings reveal the betterment of the introduced method.

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