Skripsi

 KLASIFIKASI CITRA TUMOR OTAK BERBASIS CONVOLUTIONAL NEURAL NETWORKS (CNN)

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Penilaian

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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%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307003364	T93375	T933752023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T933752023

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xviii, 65 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.360 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Jurusan Teknik Informatika
Program untuk Komputer Personal

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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