Klasifikasi Tumor Otak Menggunakan CNN Dengan Arsitektur Resnet50
DOI:
https://doi.org/10.29407/stains.v3i1.4357Keywords:
brain tumor, CNN, classification, , MRI images, ResNet-50Abstract
Penelitian ini mengusulkan penggunaan Convolutional Neural Network (CNN) dengan model ResNet-50 untuk mengklasifikasikan jenis tumor otak berdasarkan gambar MRI. Dataset terdiri dari empat kelas: Glioma, Hipofisis, Meningioma, dan Normal. Metode penelitian melibatkan pengumpulan data, preprocessing, desain arsitektur CNN, pelatihan model, dan evaluasi. Hasil pengujian menunjukkan bahwa model mampu mengklasifikasikan jenis tumor otak dengan akurasi yang memuaskan. Penerapan ResNet-50 meningkatkan kinerja dengan mengatasi masalah hilangnya gradien. Berdasarkan penelitian tersebut, klasifikasi tumor otak menggunakan CNN dengan arsitektur Resnet50 dapat mendukung deteksi dini tumor otak untuk meningkatkan akurasi diagnostik. Pada penelitian ini akurasi terbaik diperoleh sebesar 96% pada percobaan epoch ke-11.
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