@inproceedings{560e8e6b16bc4404b48e0b22edc081fb,
title = "Brain Tumor Classification Using Transfer Learning",
abstract = "One type of deadly disease is a brain tumor. To determine the presence of a brain tumor, it can be seen from an MRI image. In this research, we classified brain tumor MRI. The classification system uses transfer learning because only a few datasets are used. The Pre-Trained models used to extract features are VGG-16 and ResNet-50. Tests are carried out using several different parameters such as different batch sizes, optimizers, and learning rates. We evaluate the results using the confusion matrix. VGG-16 got the best accuracy of 0.96 using the Adam optimizer and ResNet-50 got the best accuracy of 0.94 using the RMSprop optimizer. From several different parameter variations, there is a relationship between parameter selection and accuracy results.",
keywords = "Adam, RMSProp, ResNet-50, SGD, Vgg-16, brain tumor, optimizer, pre-trained model, transfer learning",
author = "Naim Rochmawati and Hidayati, {Hanik Badriyah} and Yuni Yamasari and Wiyli Yustanti and Suartana, {I. Made} and Agus Prihanto and Aditya Prapanca",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 5th International Conference on Vocational Education and Electrical Engineering, ICVEE 2022 ; Conference date: 10-09-2022 Through 11-09-2022",
year = "2022",
doi = "10.1109/ICVEE57061.2022.9930403",
language = "English",
series = "2022 5th International Conference on Vocational Education and Electrical Engineering: The Future of Electrical Engineering, Informatics, and Educational Technology Through the Freedom of Study in the Post-Pandemic Era, ICVEE 2022 - Proceeding",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "95--99",
booktitle = "2022 5th International Conference on Vocational Education and Electrical Engineering",
address = "United States",
}