Archives
International Journal of Zoology and Applied Biosciences Research Article
Optimization and performance analysis of soft computing models for brain tumor detection
Vignesh A R, Rubala Nancy J, Devasena B, Anitha W Linisha N M
Year : 2025 | Pages: 592-596
Received on: 29/09/2025
Revised on: 26/10/2025
Accepted on: 29/11/2025
Published on: 01/12/2025
-
Vignesh A R, Rubala Nancy J, Devasena B, Anitha W Linisha N M( 2025).
Optimization and performance analysis of soft computing models for brain tumor detection
. International Journal of Zoology and Applied Biosciences, 10( 6), 592-596.
-
click to view the cite format
Abstract
Brain tumor detection is a critical task in medical diagnostics, where timely and accurate identification can significantly improve patient outcomes. Traditional manual interpretation of Magnetic Resonance Imaging (MRI) is time-consuming and prone to human error. Soft computing models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Fuzzy Logic, and hybrid Genetic Algorithm (GA)-based approaches, have demonstrated promise in automating tumor detection and classification. This study performs an optimization and comparative performance analysis of these models using publicly available MRI datasets. Preprocessing, feature extraction, and model optimization were performed to enhance classification accuracy. Performance metrics such as accuracy, sensitivity, specificity, and F1-score were evaluated. Results indicate that optimized ANN and hybrid GA-based models outperform traditional soft computing approaches in terms of accuracy and robustness. This work provides insights into the selection and optimization of soft computing techniques for reliable and efficient brain tumor detection
Keywords
Brain Tumor Detection, Soft Computing Techniques, Artificial Neural Networks, Support Vector.
-
Full Article PDF (
11)
- View HTML Article
Copy Rights
© The Author(s) 2025. This article is published by International Journal of Zoology and Applied Biosciences under the terms of the Creative Commons Attribution 4.0 International License (creativecommons.org), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
