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Diagnosis tumor otak seperti Glioma, Meningioma, dan Pituitary melalui MRI masih menghadapi tantangan seperti ketergantungan pada interpretasi manual, waktu evaluasi yang lama, dan potensi kesalahan manusia. Untuk mengatasi hal tersebut, pendekatan berbasis deep learning menawarkan solusi yang efisien dan akurat. Penelitian ini bertujuan mengembangkan model klasifikasi tumor otak berbasis deep learning menggunakan arsitektur InceptionResNetV2 dengan teknik augmentasi MixUp untuk meningkatkan akurasi dan generalisasi model. Model dilatih pada 7.023 citra MRI (Glioma: 1.621, Meningioma: 1.645, Pituitary: 1.757, No-tumor: 2.000), dengan MixUp sebagai teknik yang terbukti efektif mengurangi overfitting dan menangani ketidakseimbangan data. Model yang diusulkan mencapai akurasi tertinggi sebesar 99,70%, melampaui model lain seperti CNN dengan Image Enhancement (97,84%)


 

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InceptionResnetV2 Klasifikasi MixUp Pembelajaran Transfer Tumor Otak

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[1]
R. Mahendra, E. A. Laksana, dan S. Sukenda, “Pendekatan Transfer Learning dengan InceptionResNetV2 dan Augmentasi MixUp untuk Peningkatan Klasifikasi Tumor Otak”, Jurnal Algoritma, vol. 22, no. 1, hlm. 161–172, Mei 2025.
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