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All Journal Jurnal Komputer Indonesia
Nariswari, Naura Ulayya
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Computer Science & IT

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Classification Of Brain Tumors Using The VGG19 Method Syah, Maulidya Prastita; Kristanaya, Mirechelin; Nariswari, Naura Ulayya; Azzahra, Melinda Putri; Pratama, Alfan Rizaldy; Saputra, Wahyu S.J.
Jurnal Komputer Indonesia Vol. 3 No. 2 (2024): Desember
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jki.v3i2.677

Abstract

Brain tumor is one of the diseases that has a high mortality rate and requires early detection to increase the chance of cure. In recent years, artificial intelligence-based methods, especially Deep Learning, have shown promising performance in brain tumor classification using Magnetic Resonance Imaging (MRI) images. This study applies the VGG19 architecture, one of the Convolutional Neural Network (CNN) models, to classify brain tumor types based on MRI images. The model is trained with data that has gone through augmentation and contrast enhancement processes to improve image quality before classification. The experimental results show that the VGG19 method is able to achieve high accuracy in brain tumor classification. These findings confirm the effectiveness of VGG19 in automatically detecting brain tumors and can be a supporting solution for medical personnel in performing early diagnosis.
Co-Authors Azzahra, Melinda Putri Kristanaya, Mirechelin Pratama, Alfan Rizaldy Saputra, Wahyu S.J. Syah, Maulidya Prastita