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A Hybrid Deep Features PSO-ReliefF Based Classification of Brain Tumor

Alaa Khalid Alduraibi*

Department of Radiology, College of Medicine, Qassim University, Buraidah, 52571, Saudi Arabia

* Corresponding Author: Alaa Khalid Alduraibi. Email: email

Intelligent Automation & Soft Computing 2022, 34(2), 1295-1309.


With technological advancements, deep machine learning can assist doctors in identifying the brain mass or tumor using magnetic resonance imaging (MRI). This work extracts the deep features from 18-pre-trained convolutional neural networks (CNNs) to train the classical classifiers to categorize the brain MRI images. As a result, DenseNet-201, EfficientNet-b0, and DarkNet-53 deep features trained support vector machine (SVM) model shows the best accuracy. Furthermore, the ReliefF method is applied to extract the best features. Then, the fitness function is defined to select the number of nearest neighbors of ReliefF algorithm and feature vector size. Finally, the particle swarm optimization algorithm minimizes the fitness function to determine the optimal feature vector for model training. The proposed approach is validated by using the available online dataset. The proposed approach enhances the classification accuracy to 97.1% using the optimal concatenated deep features of DenseNet-201and DarkNet-53. Therefore, owing to the high accuracy of the proposed approach, it can be helpful to use real-time applications in the future.


Cite This Article

A. Khalid Alduraibi, "A hybrid deep features pso-relieff based classification of brain tumor," Intelligent Automation & Soft Computing, vol. 34, no.2, pp. 1295–1309, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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