Assessing attention mechanisms' impact on automatic brain tumor classification
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1276Keywords:
Convolutional Neural Network, Deep Learning, Brain TumorAbstract
Objective: To compare a conventional convolutional neural network model and its attention-enhanced counterpart. Method: We trained both models on the same dataset images of gliomas, meningiomas, pituitary adenomas, and non-tumorous images; then, we compared both models using interpretable approaches, highlighting the regions used for their predictions. Results: Our analysis found that the attention-enhanced model focused more on tumor regions, with 99% accuracy. Conclusion: The outcome of this research underscores the importance of continued exploration into advanced neural network features to elevate the standards of diagnostic accuracy and efficiency in medical practice.
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