DOI:
https://doi.org/10.64539/sjer.v1i3.2025.10Keywords:
AlexNet, Brain Tumor, Classification, CNN, GoogleNetAbstract
Early detection of brain tumors through medical imaging is crucial to improving treatment success rates. This study aims to classify brain tumors using two deep learning models, AlexNet and GoogleNet, by testing three image sizes. The dataset used consists of four classes: glioma, no tumor, meningioma, and pituitary. The test results show that the AlexNet model achieves the best accuracy of 98% at a resolution of 150x150, while GoogleNet shows stable performance with the highest accuracy of 96% at both 150x150 and 200x200 resolutions. The medium resolution (150x150) proves to be optimal for both models, providing the best balance between visual information and processing efficiency. This study highlights the potential use of AlexNet and GoogleNet in brain tumor classification, with opportunities for performance improvement through further development, such as ensemble techniques and the use of a larger dataset.
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Copyright (c) 2025 Alwas Muis, Angga Rustiawan, Babatunde Bamidele Oyeyemi, Abdul Syukur, Furizal
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