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. 2022 Aug 4:2022:1465173.
doi: 10.1155/2022/1465173. eCollection 2022.

BrainNet: Optimal Deep Learning Feature Fusion for Brain Tumor Classification

Usman Zahid  1 , Imran Ashraf  1 , Muhammad Attique Khan  2 , Majed Alhaisoni  3 , Khawaja M Yahya  4 , Hany S Hussein  5   6 , Hammam Alshazly  7
Affiliations

BrainNet: Optimal Deep Learning Feature Fusion for Brain Tumor Classification

Usman Zahid et al. Comput Intell Neurosci. .

Abstract

Early detection of brain tumors can save precious human life. This work presents a fully automated design to classify brain tumors. The proposed scheme employs optimal deep learning features for the classification of FLAIR, T1, T2, and T1CE tumors. Initially, we normalized the dataset to pass them to the ResNet101 pretrained model to perform transfer learning for our dataset. This approach results in fine-tuning the ResNet101 model for brain tumor classification. The problem with this approach is the generation of redundant features. These redundant features degrade accuracy and cause computational overhead. To tackle this problem, we find optimal features by utilizing differential evaluation and particle swarm optimization algorithms. The obtained optimal feature vectors are then serially fused to get a single-fused feature vector. PCA is applied to this fused vector to get the final optimized feature vector. This optimized feature vector is fed as input to various classifiers to classify tumors. Performance is analyzed at various stages. Performance results show that the proposed technique achieved a speedup of 25.5x in prediction time on the medium neural network with an accuracy of 94.4%. These results show significant improvement over the state-of-the-art techniques in terms of computational overhead by maintaining approximately the same accuracy.

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Conflict of interest statement

All authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
The proposed approach for the brain tumor classification.
Figure 2
Figure 2
Sample database images.
Figure 3
Figure 3
ResNet101 architecture.
Figure 4
Figure 4
Deep transfer learning process.
Figure 5
Figure 5
The proposed training process of the deep learning model for brain tumor classification.
Figure 6
Figure 6
Prediction results in the form of labeled Images (a) numerical results.
Figure 7
Figure 7
The confusion matrix of the cubic SVM after the original feature classification.
Figure 8
Figure 8
The confusion matrix of the cubic SVM after applying the particle swarm optimization (PSO)-based feature selection.
Figure 9
Figure 9
The confusion matrix of the cubic SVM after applying the differential evolution (DE)-based feature selection.
Figure 10
Figure 10
The confusion matrix of the cubic SVM after applying the optimal feature fusion.
Figure 11
Figure 11
Comparison of accuracy results with state of the art.
Figure 12
Figure 12
Comparison of the prediction time (logarithmic scale) with state of the art.
See this image and copyright information in PMC

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