Penerapan Convolutional Neural Network (CNN) dalam Klasifikasi Citra MRI untuk Deteksi Tumor Otak Manusia

Denis Lizard Sambawo Dimara, Shintyadhita Wirawan Putri, Rizky Amelia, Zalfa Ibtisamah Arishandy, Agung Mustika Rizki

Abstract


Brain tumors are deadly diseases with a high mortality rate, making early diagnosis crucial to improving patient survival rates. However, manual diagnosis through Magnetic Resonance Imaging (MRI) often requires significant time and is prone to errors. This study developed an MRI image classification method using the EfficientNetB3-based Convolutional Neural Network (CNN) architecture to detect brain tumors. The dataset used was obtained from Kaggle, consisting of 253 brain MRI images, including 98 normal and 155 abnormal images. The data were preprocessed through normalization and resizing to 224x224 pixels. The model employed transfer learning techniques using pretrained weights from ImageNet, enhanced with additional layers to improve performance. Evaluation was conducted using metrics such as accuracy, precision, recall, F1-score, AUC, as well as confusion matrix and classification report analyses. The results showed that the EfficientNetB3 model achieved an overall accuracy of 86%, demonstrating its capability to support brain tumor diagnosis processes quickly and accurately. This implementation is expected to provide a significant contribution to early detection of brain tumors and improve patient care quality in the medical field.

Keywords


CNN; Deep Learning; EfficientnetB3; Klasifikasi; Tumor Otak;

Full Text:

PDF

References


S. Jesika, S. A. P. Zai, W. P. Ananta, R. R. Sitorus, H. Syahputra, dan F. Ramadhani, "Implementasi Algoritma Naive Bayes dalam Meningkatkan Akurasi Diagnosa Penyakit Tumor Otak," Jurnal Teknik Informatika dan Sistem Informasi, vol. 11, no. 3, pp. 136–144, Sep. 2024.

O. Akbar, E. Utami, dan D. Ariatmanto, "Deteksi Tumor Otak Melalui Gambar MRI Berdasarkan Vision Transformers dengan TensorFlow dan Keras," Jurnal Informatika Universitas Pamulang, vol. 8, no. 3, pp. 385–392, Sep. 2023, DOI: 10.32493/informatika.v8i3.32707.

M. N. Winnarto, M. Mailasari, dan A. Purnamawati, "Klasifikasi Jenis Tumor Otak Menggunakan Arsitektur MobileNetV2," Jurnal SIMETRIS, vol. 13, no. 2, Nov. 2022.

R. S. Passa, S. Nurmaini, dan D. P. Rini, "Deteksi Tumor Otak pada Magnetic Resonance Imaging Menggunakan YOLOv7," Jurnal Ilmiah MATRIK, vol. 25, no. 2, pp. 114–122, Aug. 2023.

A. Digdoyo, T. Surawan, A. S. B. Karno, D. R. Irawati, dan Y. Efendi, "Deteksi Dini Tumor Otak Menggunakan Metode Deep Learning Arsitektur CNN ResNet-152," Jurnal Teknologi, vol. 9, no. 2, pp. 114–122, 2022, DOI: https://doi.org/10.31479/jtek.v9i2.128.

Passa, E., Lestari, T., dan Andriani, R., "Peranan MRI dalam deteksi dini tumor otak: Studi komparasi," Journal of Medical Imaging and Diagnosis, vol. 11, no. 4, pp. 123-130, 2023.

Jesika, S., Ramdani, F., dan Fadilah, N., "Klasifikasi tumor otak menggunakan algoritma Naive Bayes pada citra MRI," Journal of Computer and Health Informatics, vol. 15, no. 1, pp. 12-20, 2024.

Kaushik, P. (2023). Deep learning and machine learning to diagnose melanoma. International Journal of Research in Science and Technology, 13(01), 58-72. https://doi.org/10.37648/ijrst.v13i01.008

Oumarou, H., Siradj, Y., Rizal, R., & Candra, F. (2024). Stabilization of image classification accuracy in hybrid quantum-classical convolutional neural network with ensemble learning. Innovation in Research of Informatics (INNOVATICS), 6(1). https://doi.org/10.37058/innovatics.v6i1.10437

Kandel, I., Castelli, M., & Popovič, A. (2020). Musculoskeletal images classification for detection of fractures using transfer learning. Journal of Imaging, 6(11), 127. https://doi.org/10.3390/jimaging6110127

Chen, B., Huang, Y., Xia, Q., & Zhang, Q. (2020). Nonlocal spatial attention module for image classification. International Journal of Advanced Robotic Systems, 17(5). https://doi.org/10.1177/1729881420938927

Tan, M. and Le, Q. V. (2019). Efficientnet: rethinking model scaling for convolutional neural networks.. https://doi.org/10.48550/arxiv.1905.11946




DOI: https://doi.org/10.31284/j.kernel.2023.v4i2.6960

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Denis Lizard Sambawo Dimara, Shintyadhita Wirawan Putri, Rizky Amelia, Zalfa Ibtisamah Arishandy, Agung Mustika Rizki

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Diindeks oleh:
Google Scholar logo Dimensions Logo