Classification of Magnetic Resonance Imaging (MRI) Images of Brain Tumors Using Convolutional Neural Network (CNN) Methods with VGG-16 and Xception Architectures
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Abstract
rain tumors are critical medical conditions that require early diagnosis to improve treatment
outcomes. Magnetic Resonance Imaging (MRI) is widely utilized for brain tumor detection due to its
ability to produce high-resolution images of soft tissues. Nevertheless, manual interpretation of MRI
images presents several challenges, including time inefficiency and variability among observers. To
address these issues, this study applies the Convolutional Neural Network (CNN) approach using
VGG-16 and Xception architectures to classify brain tumor MRI images and to evaluate their
performance comparatively. The dataset comprises 2,877 MRI images categorized into four classes:
glioma tumor, meningioma tumor, pituitary tumor, and no tumor. Preprocessing stages include
resizing images to 224×224 pixels and dividing the dataset into training, validation, and testing sets
with a ratio of 80:10:10. Model performance is assessed using accuracy, precision, recall, and F1-score
metrics. Experimental results indicate that the VGG-16 architecture achieves an accuracy of 92%, while
the Xception architecture records an accuracy of 91%.
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References
(DWT) untuk Klasifikasi Penyakit Tumor Otak”, [Online]. Available:
http://med.harvard.edu/AANLIB/.
[2] I. B. L. M. Suta, M. Sudarma, and I. N. Satya Kumara, “Segmentasi Tumor Otak
Berdasarkan Citra Magnetic Resonance Imaging Dengan Menggunakan Metode U© Melania Erika Andolo1, Herman Bedi Agtriadi2, Meilia Nur Indah Susanti3
NET,” Majalah Ilmiah Teknologi Elektro, vol. 19, no. 2, p. 151, Dec. 2020, doi:
10.24843/mite.2020.v19i02.p05.
[3] D. Husen, “Klasifikasi Citra MRI Tumor Otak Menggunakan Metode Convolutional
Neural Network,” bit-Tech, vol. 7, no. 1, pp. 143–152, Aug. 2024, doi:
10.32877/bt.v7i1.1576.
[4] M. Z. Basri, Muhammad Gabriel Somoal, and Rafiq Setyo Aji, “Deteksi Tumor Otak
pada Citra Magnetic Resonance Imaging (MRI) Menggunakan Arsitektur MobileNet
dengan Optimizer Adam,” JEKIN - Jurnal Teknik Informatika, vol. 5, no. 2, pp. 539–550,
Jun. 2025, doi: 10.58794/jekin.v5i2.1394.
[5] P. Rosyani, S. Saprudin, and R. Amalia, “Klasifikasi Citra Menggunakan Metode
Random Forest dan Sequential Minimal Optimization (SMO),” Jurnal Sistem dan
Teknologi Informasi (Justin), vol. 9, no. 2, p. 132, Apr. 2021, doi: 10.26418/justin.v9i2.44120.
[6] E. Hari Rachmawanto, “Convolutional Neural Network (CNN) untuk Klasifikasi Citra
Penyakit Diabetes Retinopathy,” SKANIKA: Sistem Komputer dan Teknik Informatika, vol.
5, no. 2, pp. 167–176, 2022.
[7] M. Setiono, “Klasifikasi Penyakit Antraknosa Citra Cabai Rawit Dengan Metode
Convolutional Neural Network (CNN),” Hal; Jl. Jembatan Merah No.84 C Gejayan
Yogyakarta, vol. 11, no. 2, 2024.
[8] I. Elzandy et al., “KLASIFIKASI VARIETAS ANGGUR BERDASARKAN CITRA DAUN
MENGGUNAKAN VGG16-XGBOOST,” 2025.
[9] D. Prasetia, B. Hanggara, R. Naufaldihanif, A. Clark Peter Wijaya, and A. Ibrahim,
“KLASIFIKASI KONDISI KEBERSIHAN SUNGAI DARI CITRA DIGITAL
MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DENGAN
ALGORITMA INCEPTION V3 DAN XCEPTION,” 2025.
[10] K. Azmi, S. Defit, and U. Putra Indonesia YPTK Padang Jl Raya Lubuk BegalungPadang-Sumatera Barat, “Implementasi Convolutional Neural Network (CNN) Untuk
Klasifikasi Batik Tanah Liat Sumatera Barat,” vol. 16, no. 1, p. 2023.
[11] A. Yusuf, R. Cahya Wihandika, and C. Dewi, “Klasifikasi Emosi Berdasarkan Ciri Wajah
Menggunakan Convolutional Neural Network,” 2019. [Online]. Available: http://jptiik.ub.ac.id
[12] R. Rizkyatul Basir et al., “EVALUASI KINERJA MODEL KLASIFIKASI PEMBELIAN
SEPEDA MENGGUNAKAN ALGORITMA RANDOM FOREST,” 2025, doi:
10.1007/s11749-016.