Research Article Open Access

Grasshopper Optimization Algorithm-Generative Adversarial Network for Lung Cancer Detection and Classification

Sukruth Gowda1 and A Jayachandran1
  • 1 Presidency University, India

Abstract

Lung cancer is one of the most dangerous deadly diseases for individuals worldwide. Thus, the survival rate is low due to the difficulty in detecting lung cancer at advanced stages like symptoms; thus, prominence for early diagnosis is important. The detection and treatment of lung cancer is having great importance for early diagnosis. The existing Convolution Neural Network (CNN) based deep learning methods showed tuning was the problem of choosing a set of hyperparameters for the learning algorithm and included outliers that affect the classification result. Therefore, the present research work aims to utilize Grasshopper Optimization Algorithm (GOA) effectively to solve global unconstrained and constrained optimization issues. Additionally, performing training using the Generative Adversarial Network (GAN) model that controlled the behavior of the classifier during training showed a significant impact. The results showed that the proposed method gives better results in terms of accuracy of 98.89% when compared to the existing models such as KNG-CNN of 87.3%, mask region-based CNN of 97.68%, Transferable Texture CNN of 96.69%, Fuzzy Particle Swarm Optimization (FPSO) CNN of 95.62% and E-CNN method of 97%.

Journal of Computer Science
Volume 18 No. 3, 2022, 227-232

DOI: https://doi.org/10.3844/jcssp.2022.227.232

Submitted On: 17 January 2022 Published On: 9 April 2022

How to Cite: Gowda, S. & Jayachandran, A. (2022). Grasshopper Optimization Algorithm-Generative Adversarial Network for Lung Cancer Detection and Classification. Journal of Computer Science, 18(3), 227-232. https://doi.org/10.3844/jcssp.2022.227.232

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Keywords

  • Convolution Neural Network
  • Generative Adversarial Network
  • Grasshopper Optimization Algorithm
  • Hyper Parameters
  • Lung Cancer
  • Outliers
  • Unconstrained and Constrained Optimization Issues