Pengembangan Model Deteksi Tumor Otak pada Magnetic Resonance Imaging Menggunakan Arsitektur YOLOv10
DOI:
https://doi.org/10.30595/sainteks.v21i2.23989Keywords:
Brain Tumor, Deep Learning, You Only Look OnceAbstract
Dengan meningkatnya kebutuhan akan diagnosis yang cepat dan akurat dalam bidang medis, model deteksi berbasis deep learning menawarkan solusi yang menjanjikan. Penelitian ini bertujuan untuk mengembangkan model deteksi tumor otak pada citra Magnetic Resonance Imaging (MRI) menggunakan arsitektur YOLOv10. YOLOv10 dipilih karena kemampuannya dalam melakukan deteksi objek secara real-time dengan tingkat akurasi yang tinggi. Dalam penelitian ini, dataset MRI otak yang terdiri dari 1003 gambar digunakan untuk melatih model. Proses pelatihan dilakukan dengan menggunakan berbagai jumlah epoch untuk mengidentifikasi parameter yang optimal. Hasil menunjukkan bahwa model YOLOv10 mampu mendeteksi tumor otak dengan tingkat presisi yang tinggi, dengan metrics precision sebesar 97,88%, recall 95,24%, dan mAP50 sebesar 95,84%.pada epoch 200. Model ini diharapkan dapat digunakan sebagai alat bantu bagi para profesional medis dalam mendeteksi tumor otak secara lebih efisien dan efektif, serta memberikan kontribusi signifikan dalam bidang diagnosa penyakit menggunakan teknologi berbasis kecerdasan buatan.
References
Ahmad, S. and Choudhury, P.K. (2022) ‘On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images’, IEEE Access, pp. 59099–59114. Available at: https://doi.org/10.1109/ACCESS.2022.3179376.
Akbar, F., Rais, A.N., Sobari, I.A., Zuama, R.A. and Rudiarto, B. (2019) ‘Analisis Performa Algoritma Naive Bayes Pada Deteksi Otomatis Citra Mri’, JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), pp. 37–42. Available at: https://doi.org/10.33480/jitk.v5i1.586.
Aleid, A., Alhussaini, K., Alanazi, R., Altwaimi, M., Altwijri, O. and Saad, A.S. (2023) ‘Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images’, Applied Sciences (Switzerland). Available at: https://doi.org/10.3390/app13063808.
Anushkannan, N.K., Kumbhar, V.R., Maddila, S.K., Kolli, C.S., Vidhya, B. and Vidhya, R.G. (2022) ‘YOLO Algorithm for Helmet Detection in Industries for Safety Purpose’, 3rd International Conference on Smart Electronics and Communication, ICOSEC 2022 - Proceedings, pp. 225–230. Available at: https://doi.org/10.1109/ICOSEC54921.2022.9952154.
Asad, R., Rehman, S. ur, Imran, A., Li, J., Almuhaimeed, A. and Alzahrani, A. (2023) ‘Computer-Aided Early Melanoma Brain-Tumor Detection Using Deep-Learning Approach’, Biomedicines. Available at: https://doi.org/10.3390/biomedicines11010184.
Astuti, L.W. (2019) ‘Ekstrasi Fitur Citra MRI Otak Menggunakan Data Wavelet Transform (DWT) untuk Klasifikasi Penyakit Tumor Otak’, Jurnal Ilmiah Informatika Global. Available at: https://doi.org/10.36982/jig.v10i2.854.
Maharana, K., Mondal, S. and Nemade, B. (2022) ‘A review: Data pre-processing and data augmentation techniques’, Global Transitions Proceedings, pp. 91–99. Available at: https://doi.org/10.1016/j.gltp.2022.04.020.
MRI, B. (2023) ‘MRI Dataset’. Roboflow. Available at: https://universe.roboflow.com/brain-mri/mri-rskcu.
Mulyana, D.I. and Rowis, M.A.I. (2022) ‘Optimization of Text Mining Detection of Tajweed Reading Laws Using the Yolov8 Method on the Qur’an’, QALAMUNA: Jurnal Pendidikan, Sosial, dan Agama, pp. 1089–1110. Available at: https://doi.org/10.37680/qalamuna.v14i2.3866.
Musallam, A.S., Sherif, A.S. and Hussein, M.K. (2022) ‘A New Convolutional Neural Network Architecture for Automatic Detection of Brain Tumors in Magnetic Resonance Imaging Images’, IEEE Access, pp. 2775–2782. Available at: https://doi.org/10.1109/ACCESS.2022.3140289.
Ottom, M.A., Rahman, H.A. and Dinov, I.D. (2022) ‘Deep learning approach for 2D MRI brain tumor segmentation’. IEEE. Available at: https://doi.org/https://doi.org/10.1109/JTEHM.2022.3176737.
Padilla, R., Netto, S.L. and Da Silva, E.A.B. (2020) ‘A Survey on Performance Metrics for Object-Detection Algorithms’, International Conference on Systems, Signals, and Image Processing, pp. 237–242. Available at: https://doi.org/10.1109/IWSSIP48289.2020.9145130.
Saraswathi, V. and Gupta, D. (2019) ‘Classification of Brain Tumor using PCA-RF in MR Neurological Images’, 2019 11th International Conference on Communication Systems and Networks, COMSNETS 2019, pp. 440–443. Available at: https://doi.org/10.1109/COMSNETS.2019.8711010.
Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A. and Bray, F. (2021) ‘Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries’, CA: A Cancer Journal for Clinicians, pp. 209–249. Available at: https://doi.org/10.3322/caac.21660.
Tika, A.M. and Qudsiah, N.A. (2022) ‘Klasifikasi Tumor Otak Menggunakan Ekstraksi Fitur HOG dan Support Vector Machine’, Jurnal Infortech, pp. 45–50. Available at: https://ejournal.bsi.ac.id/ejurnal/index.php/infortech/article/view/12813.
Toufiq, D.M., Sagheer, A.M. and Veisi, H. (2021) ‘A Review on Brain Tumor Classification in MRI Images’, Turkish Journal of Computer and Mathematics Education, pp. 1958–1969.
Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J. and Ding, G. (2024) ‘YOLOv10: Real-Time End-to-End Object Detection’. Available at: http://arxiv.org/abs/2405.14458.
Zoph, B., Cubuk, E.D., Ghiasi, G., Lin, T.Y., Shlens, J. and Le, Q. V. (2020) ‘Learning Data Augmentation Strategies for Object Detection’, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 566–583. Available at: https://doi.org/10.1007/978-3-030-58583-9_34.
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