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. 2024 Mar 14:10:e1878.
doi: 10.7717/peerj-cs.1878. eCollection 2024.

Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability

Abdullah A Asiri  1 , Ahmad Shaf  2 , Tariq Ali  2 , Muhammad Aamir  2 , Muhammad Irfan  3 , Saeed Alqahtani  1
Affiliations

Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability

Abdullah A Asiri et al. PeerJ Comput Sci. .

Abstract

Hyperparameter tuning plays a pivotal role in the accuracy and reliability of convolutional neural network (CNN) models used in brain tumor diagnosis. These hyperparameters exert control over various aspects of the neural network, encompassing feature extraction, spatial resolution, non-linear mapping, convergence speed, and model complexity. We propose a meticulously refined CNN hyperparameter model designed to optimize critical parameters, including filter number and size, stride padding, pooling techniques, activation functions, learning rate, batch size, and the number of layers. Our approach leverages two publicly available brain tumor MRI datasets for research purposes. The first dataset comprises a total of 7,023 human brain images, categorized into four classes: glioma, meningioma, no tumor, and pituitary. The second dataset contains 253 images classified as "yes" and "no." Our approach delivers exceptional results, demonstrating an average 94.25% precision, recall, and F1-score with 96% accuracy for dataset 1, while an average 87.5% precision, recall, and F1-score, with accuracy of 88% for dataset 2. To affirm the robustness of our findings, we perform a comprehensive comparison with existing techniques, revealing that our method consistently outperforms these approaches. By systematically fine-tuning these critical hyperparameters, our model not only enhances its performance but also bolsters its generalization capabilities. This optimized CNN model provides medical experts with a more precise and efficient tool for supporting their decision-making processes in brain tumor diagnosis.

Keywords: Brain tumor diagnosis; Decision-making processes; Feature extraction; Hyperparameter tuning; Model complexity; Optimization techniques; Spatial resolution.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. Flowchart of the proposed work.
Figure 2
Figure 2. Sample images of dataset 1.
Image source credit: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset, CC0.
Figure 3
Figure 3. Working architecture of fine-tuned hyperparametric CNN model for dataset 1.
Figure 4
Figure 4. Working architecture of fine-tuned hyperparametric CNN model for dataset 2.
Figure 5
Figure 5. The confusion matrix generated by the proposed model on the testing data from dataset 1.
Figure 6
Figure 6. The confusion matrix generated by the proposed model on the testing data from dataset 2.
Figure 7
Figure 7. Accuracy graph for dataset 1.
Figure 8
Figure 8. Accuracy graph for dataset 2.
Figure 9
Figure 9. Loss graph for dataset 1.
Figure 10
Figure 10. Loss graph for dataset 2.
Figure 11
Figure 11. ROC graph for dataset 1.
Figure 12
Figure 12. ROC graph for dataset 2.
Figure 13
Figure 13. Comparing the accuracy percentage proportions across various hyperparameter configurations of the CNN model on dataset 1.
Figure 14
Figure 14. Comparing the accuracy percentage proportions across various hyperparameter configurations of the CNN model on dataset 2.
See this image and copyright information in PMC

References

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