Published January 30, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

A Survey on Liver Cancer Detection Using Hyperfusion of CNN and SVM in Machine Learning

Creators

  • 1. Student, Department of Computer Science and Engineering Kalaignarkarunanidhi Institute of Technology, Coimbatore (Tamil Nadu), India.

Contributors

Contact person:

Researcher:

  • 1. Assistant Professor, Department of Computer Science and Engineering Kalaignarkarunanidhi Institute of Technology, Coimbatore (Tamil Nadu), India.
  • 2. Student, Department of Computer Science and Engineering Kalaignarkarunanidhi Institute of Technology, Coimbatore (Tamil Nadu), India.

Description

Abstract: Since liver cancer ranks among of the most aggressive renditions of the disease, improving patient outcomes requires early identification. We propose an inventive tactic to liver cancer detection by integrating CNN and SVM. CNNs, known for their powerful feature extraction capabilities, are particularly effective in analysing complex medical images. SVMs, on the other hand, are efficient classifiers that can separate data points in high-dimensional spaces with accuracy. By merging the feature extraction strength of CNN with the classification efficiency of SVM, the proposed model aims to enhance liver cancer detection accuracy and robustness. The experimental results reveal that the fused CNN-SVM model significantly surpasses the performance of standalone CNN and SVM models, achieving a high detection accuracy of 95.2%. This hybrid method offers a promising direction for improving the precision of computer-aided diagnosis systems, contributing to more effective and reliable liver cancer detection methods that can assist healthcare professionals in making timely decisions.

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Additional details

Identifiers

DOI
10.54105/ijpmh.B1051.05020125
EISSN
2582-7588

Dates

Accepted
2025-01-15
Manuscript received on 21 November 2024 | First Revised Manuscript received on 30 November 2024 | Second Revised Manuscript received on 13 December 2024 | Manuscript Accepted on 15 January 2025 | Manuscript published on 30 January 2025.

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