- Ida Bagus Leo Mahadya Suta Udayana University
- Rukmi Sari Hartati
- Yoga Divayana
Brain tumors are one of the most deadly diseases, one of the most common types is glioma, about 6 out of 100,000 patients are glioma sufferers. Digital imagery through Magnetic Resonance Imaging (MRI) is one method to help doctors analyze and classify brain tumor types. However, manual classification requires a long time and has a high risk of errors, so an automatic and accurate method is needed to classify MRI images. Convolutional Neural Network (CNN) is one of the solutions for automatic classification in MRI images. CNN is a deep learning algorithm that has the ability to learn on its own from the previous case. And from the research that has been done, the results obtained that CNN is able to complete the classification of brain tumors with high accuracy. Accuracy enhancements are obtained by developing the CNN algorithm either by determining the kernel value and / or activation function.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License
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