Evaluation of transfer learning for brain tumor detection in medical images
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1302Keywords:
Transfer Learning, Convolutional Neural Network, Brain TumorAbstract
Objective: With the increased feasibility of applying convolutional neural networks (CNNs), the goal was to evaluate the use of this technology for detecting brain tumors in computerized magnetic resonance images. Method: Two distinct CNN models were developed, one using transfer learning and the other without, to classify the occurrence of brain tumors. Results: The model without transfer learning achieved an accuracy of 99.67%, with a sensitivity of 100% and specificity of 99.34%. The model using transfer learning achieved an accuracy of 98%, with a sensitivity of 98.32% and specificity of 97.69%. Conclusion: This study highlights the efficacy of CNNs in detecting brain tumors, suggesting the use of intelligent systems as auxiliary tools.
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