A Comparative Analysis of GOA (Grasshopper Optimization Algorithm) Adversarial Deep Belief Neural Network for Renal Cell Carcinoma: Kidney Cancer Detection & Classification

Authors

  • M. Sughasiny Associate Professor, Department of MCA, School of Sciences and Humanities Management, Dhanalakshmi Srinivasan University, Samayapuram, Trichy-612112
  • K. K. Thyagharajan Professor & Dean Research, R.M.D Engineering College
  • A. Karthikayen Professor, Department of ECE, Sri Sai Institute of Technology & Science, Rayachoty-516270. Annamaya Dt., Andhrapradesh, India
  • K. Sangeetha Associate Professor, Department of CSE, Panimalar Engineering College, India
  • K. Sivakumar Professor, Department of Mechanical Engineering, P.T. Lee Chengalvaraya Naicker College of Engineering and Technology, Kanchipuram

Keywords:

Deep Belief Neural Network, Deep Adversarial Network, Grasshopper Optimization Algorithm, Hyper Parameters, Renal Cell Carcinoma- Kidney Cancer, Outliers, restricted and unrestricted Optimization Issues

Abstract

Renal Cell Carcinoma is a kind of cancer that affects the kidneys. Kidney cancers, also known as RCC, are some of the most devastating illnesses that affect people all over the globe. As a result of the difficulties in recognising kidney cancer at late stages, such as symptoms, the life expectancy is poor; hence, the need of early detection is critical. Kidney cancer detection and therapy are extremely important for early. Existing deep learning approaches based on Deep Belief Neural Networks (DBN) revealed that tuning was an issue of selecting a group of hyper - parameters for the process of learning and contained outliers that influenced the classification outcome. As a result, the goal of this research is to successfully use the Grasshopper Optimization Algorithms (GOA) to perspective of the world unrestricted and restricted multi objective optimization problem. Furthermore, training with the Deep Adversarial Belief Network (DABN) model, that regulated the classifier's behaviour throughout learning, had a substantial effect. The findings indicated that the suggested strategy outperforms current approaches like as, E-CNN method (97%), Fuzzy Particle Swarm Optimization (FPSO) CNN (91.45%), Transferable Texture CNN (98.25%), mask region-based CNN (87.86%) and KNG-CNN (92.4%) in terms of accuracy.

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Published

27.12.2023

How to Cite

Sughasiny, M. ., Thyagharajan, K. K., Karthikayen, A. ., Sangeetha, K. ., & Sivakumar, K. . (2023). A Comparative Analysis of GOA (Grasshopper Optimization Algorithm) Adversarial Deep Belief Neural Network for Renal Cell Carcinoma: Kidney Cancer Detection & Classification. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 43–48. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4200

Issue

Vol. 12 No. 9s (2024)

Section

Research Article

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