Klasifikasi Tumor Otak pada Citra MRI Menggunakan Transfer Learning EfficientNetB1 dan Visualisasi Grad-CAM
DOI:
https://doi.org/10.33998/jms.2026.6.1.2782Kata Kunci:
tumor otak, MRI, EfficientNet, transfer learning, Grad-CAM, pipeline.Abstrak
Magnetic resonance imaging (MRI) menyediakan kontras anatomi yang kaya untuk penilaian tumor otak, tetapi interpretasi rutin tetap memerlukan waktu dan ketelitian tinggi. Penelitian ini membangun pipeline klasifikasi empat kelas citra MRI otak (glioma, meningioma, pituitary, dan tidak ada tumor) dengan menggabungkan pemotongan area otak otomatis, augmentasi data, serta transfer learning menggunakan EfficientNetB1. Hasil eksperimen menunjukkan performa yang sangat kuat dengan raihan akurasi keseluruhan mencapai 0,99 (99%) pada data uji. Secara spesifik, model mencapai skor F1 sebesar 1,00 pada kelas tidak ada tumor, 0,99 pada kelas pituitary, serta 0,98 untuk kelas glioma dan meningioma. Selain laporan kuantitatif, penggunaan visualisasi Grad-CAM membuktikan bahwa prediksi model berlandaskan pada wilayah anatomi yang masuk akal secara klinis. Temuan ini menunjukkan bahwa kombinasi arsitektur yang efisien dan preprocessing yang tepat mampu menghasilkan sistem deteksi tumor otak yang akurat sekaligus dapat diinterpretasikan secara visualUnduhan
Referensi
D. N. Louis et al., “The 2021 WHO Classification of Tumors of the Central Nervous System: a summary,” Neuro-Oncology, vol. 23, no. 8, pp. 1231–1251, Aug. 2021, doi: 10.1093/neuonc/noab106.
C. Horbinski, T. Berger, R. J. Packer, and P. Y. Wen, “Clinical implications of the 2021 edition of the WHO classification of central nervous system tumours,” Nat Rev Neurol, vol. 18, no. 9, pp. 515–529, Sep. 2022, doi: 10.1038/s41582-022-00679-w.
M. K. Abd-Ellah, A. I. Awad, A. A. M. Khalaf, and H. F. A. Hamed, “A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned,” Magnetic Resonance Imaging, vol. 61, pp. 300–318, Sep. 2019, doi: 10.1016/j.mri.2019.05.028.
G. Litjens et al., “A Survey on Deep Learning in Medical Image Analysis,” 2017, doi: 10.48550/ARXIV.1702.05747.
S. Saeedi, S. Rezayi, H. Keshavarz, and S. R. Niakan Kalhori, “MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques,” BMC Med Inform Decis Mak, vol. 23, no. 1, p. 16, Jan. 2023, doi: 10.1186/s12911-023-02114-6.
H. Listiani, S. N. Asia, S. Sepriano, and L. Judijanto, Deep Learning: Konsep, Arsitektur, dan Implementasi. PT. Sonpedia Publishing Indonesia, 2025.
M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” 2019, doi: 10.48550/ARXIV.1905.11946.
E. Tjoa and C. Guan, “A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI,” IEEE Trans. Neural Netw. Learning Syst., vol. 32, no. 11, pp. 4793–4813, Nov. 2021, doi: 10.1109/TNNLS.2020.3027314.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization,” Int J Comput Vis, vol. 128, no. 2, pp. 336–359, Feb. 2020, doi: 10.1007/s11263-019-01228-7.
A. D A, M. S. Shekar, A. Bharadwaj, N. Vineeth, and M. L. Neelima, “Deep Learning in Medical Image Analysis: A Survey,” in 2024 International Conference on Innovation and Novelty in Engineering and Technology (INNOVA), Vijayapura, India: IEEE, Dec. 2024, pp. 1–5. doi: 10.1109/INNOVA63080.2024.10847040.
W. S. Mada Sanjaya, Deep Learning Citra Medis Berbasis Pemrograman Python-Penerbit Bolabot. Bolabot, 2023.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, May 2017, doi: 10.1145/3065386.
M. Shafiq and Z. Gu, “Deep Residual Learning for Image Recognition: A Survey,” Applied Sciences, vol. 12, no. 18, p. 8972, Sep. 2022, doi: 10.3390/app12188972.
M. K. Insani and D. B. Santoso, “Perbandingan Kinerja Model Pre-Trained CNN (VGG16, RESNET, dan INCEPTIONV3) untuk Aplikasi Pengenalan Wajah pada Sistem Absensi Karyawan,” jimik, vol. 5, no. 3, pp. 2612–2622, Sep. 2024, doi: 10.35870/jimik.v5i3.925.
A. Sasongko, M. S. Maulana, A. Mustopa, and W. Nugraha, “Automatic Wound Image Segmentation with U-Net Model for Smartphone Application,” JEPIN, vol. 10, no. 2, p. 267, Aug. 2024, doi: 10.26418/jp.v10i2.78548.
F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. H. Maier-Hein, “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation,” Nat Methods, vol. 18, no. 2, pp. 203–211, Feb. 2021, doi: 10.1038/s41592-020-01008-z.
M. Harahap and A. M. Husein, “Peneraparan Efficient-Net Dalam Mengklasifikasi Kanker Kulit,” PUBLIS PENERBIT UNPRI PRESS, vol. 1, no. 1, Jan. 2024, Accessed: Jan. 08, 2026. [Online]. Available: https://jurnal.unprimdn.ac.id/index.php/isbn/article/view/5405
H.-C. Shin et al., “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1285–1298, May 2016, doi: 10.1109/TMI.2016.2528162.
D. J. Rumala, “Brain Disease Classification in MRI Images Using Deep Learning Based on Deep-Stacked Models and Enhanced Feature Representation,” Institut Teknologi Sepuluh Nopember, 2024.
S. Das and S. Das, “A Comparative Analysis of Optimization Methods for Classification on Various Datasets,” 2025.
S. Yang, W. Xiao, M. Zhang, S. Guo, J. Zhao, and F. Shen, “Image Data Augmentation for Deep Learning: A Survey,” 2022, arXiv. doi: 10.48550/ARXIV.2204.08610.
Haibo He and E. A. Garcia, “Learning from Imbalanced Data,” IEEE Trans. Knowl. Data Eng., vol. 21, no. 9, pp. 1263–1284, Sep. 2009, doi: 10.1109/TKDE.2008.239.
M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Information Processing & Management, vol. 45, no. 4, pp. 427–437, Jul. 2009, doi: 10.1016/j.ipm.2009.03.002.
