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DETEKSI TUMOR OTAK PADA CITRA DIGITAL MRI MENGGUNAKAN METODE FASTER REGION-BASED CONVOLUTIONAL NEURAL NETWORK

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This research evaluates the performance of several Faster R-CNN models with different hyperparameter configurations for brain tumor detection tasks. Evaluation results show that Model with a learning rate of 0.01, batch size of 4, Resnet50 backbone, and a dataset ratio of 80:20, achieved the best results. This model achieved mAP at IoU thresholds of 0.3, 0.4, and 0.5 of 0.9503, 0.9377, and 0.8992, respectively. This configuration proved to provide an optimal balance between learning speed, model stability, and sufficient data for training and evaluation. Recommendations for further research include experimenting with other hyperparameters, using more modern backbones, and deeper validation to enhance model performance.

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Inventory Code Barcode Call Number Location Status
2407005168T154153T1541532024Central Library (References)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1541532024
Publisher
Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xviii, VI-2 hlm.; ilus.; tab, 29 cm.
Language
Indonesia
ISBN/ISSN
-
Classification
006.370 7
Content Type
Text
Media Type
unmediated
Carrier Type
other (computer)
Edition
-
Subject(s)
Prodi Teknik Informatika
Visi Komputer
Specific Detail Info
-
Statement of Responsibility
Other version/related
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