KLASIFIKASI CITRA TUMOR OTAK BERBASIS CONVOLUTIONAL NEURAL NETWORKS (CNN)

HUTAHAEAN, JERREL ADRIEL ARCHIBALD and Fachrurrozi, Muhammad and Darmawahyuni, Annisa (2023) KLASIFIKASI CITRA TUMOR OTAK BERBASIS CONVOLUTIONAL NEURAL NETWORKS (CNN). Undergraduate thesis, Sriwijaya University.

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Abstract

Brain tumors are still a dangerous and deadly disease that can affect anyone, including children. Brain tumors can be detected by Magnetic Resonance Imaging (MRI) examination. This study aims to classify brain tumors and non-brain tumors from MRI image processing results using the Convolution Neural Networks (CNN) method. This research uses the architecture of VGG-16, ResNet-50, InceptionV3, MobileNet, EfficientNetB7, and various configurations on the learning rate and batch size in building the best CNN model. The dataset used in this research is the Brain MRI Images for Brain Tumor Detection dataset, which contains 253 images. The test results in this study produced the best CNN model using VGG-16 architecture, learning rate = 0.0001, and batch size = 16 with an accuracy value of 100%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Tumor Otak, Klasifikasi, Citra MRI, Convolutional Neural Networks, Brain MRI Images for Brain Tumor Dataset
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Jerrel Adriel Archibald Hutahaean
Date Deposited:	05 Apr 2023 06:03
Last Modified:	05 Apr 2023 06:03
URI:	http://repository.unsri.ac.id/id/eprint/93375

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