Improving automatic classification of brain tumors with deep learning techniques
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1253Keywords:
Deep Learning, Automatic Classification, Brain tumorsAbstract
The accuracy in the automatic classification of brain tumors plays a crucial role in the method's reliability for healthcare applications. Classification errors can lead to inaccurate diagnoses, resulting in inappropriate and potentially harmful approaches. Objective: To propose an approach aimed at minimizing classification errors. Method: We developed a two-stage convolutional neural network model: first, four binary models for tumors with significant differentiation challenges; then, an Ensemble model for multiclass classification. Additionally, we employed a technique to interpret model predictions and identify regions of interest in medical images. Results: The proposed approach achieves an accuracy of 98%. Conclusion: This work contributes to applying deep learning in brain tumor classification, emphasizing the importance of transparent and robust approaches for precision and safety in predictions.
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