Iranian Journal of Biomedical Engineering (IJBME)
نشریه علمی مهندسی پزشکی زیستی

Accuracy and Precision Improving of Brain Tumors Diagnosis in MRI Images using Deep Learning Methods based on Swarm Intelligence Algorithms

Document Type : Full Research Paper

Authors

Manijeh-Sadat Banifatemeh 1
Reza Ghaemi 1
Amir Tabatabaee, 2

1 Department of Computer Engineering, Qu.C., Islamic Azad University, Quchan, Iran

2 Department of Nursing, Qu.C., Islamic Azad University, Quchan, Iran

https://doi.org/10.22041/ijbme.2026.2077589.2008
Abstract
Accurate and rapid brain tumors diagnosis of brain tumors in magnetic resonance imaging (MRI) is one of the major challenges in the health and medical fields, especially in radiology. The deep learning models have been widely used in the medical field due to their high capabilities in extracting complex and nonlinear features from image data. However, deep learning models may have unstable results due to their strong dependence on model settings and sensitivity to input data quality. In this study, in order to improve the accuracy and precision in diagnosis of brain cancer tumors, a deep learning model is proposed based on two swarm intelligence algorithms, in which the particle swarm algorithm is employed to adjust the hyper parameters of the deep neural network and the ant colony algorithm is applied to select the most effective features of MRI images. Experimental results of tests on the Kaggle MRI brain tumor as benchmark dataset indicate 95% accuracy and 91% accuracy of brain tumor diagnosis obtained from the proposed model in this study, as well as improved performance compared to conventional deep learning methods.

Keywords

Brain Tumor Diagnosis
Deep Learning
Swarm Intelligence Meta-heuristic Algorithm
Image Processing
MRI Image Data
Artificial Neural Network
Subjects
Iranian Journal of Biomedical Engineering (IJBME)
Volume 19, Issue 4
Winter 2026
Pages 273-288

  • Receive Date 12 November 2025
  • Revise Date 04 May 2026
  • Accept Date 04 July 2026

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