Optimasi Deteksi Tumor Otak Menggunakan Adaptive Multiscale Retinex dan YOLOV10 Pada Citra Digital
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Sep 20, 2024
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The incidence of brain tumors in Indonesia continues to rise annually, affecting both adults and children. Therefore, an effective detection system is required to differentiate MRI images showing the presence of brain tumors from those that do not. This distinction is crucial in the analysis and diagnosis of medical images. This study aims to optimize brain tumor detection in digital images by combining the Adaptive Multiscale Retinex (AMSR) method with the YOLOv10 algorithm. AMSR enhances the quality and contrast of brain images, making critical details more visible. The method adjusts the scale and intensity of image lighting to address uneven illumination and improve the visibility of brain structures. YOLOv10 is recognized for its speed and accuracy in real-time object detection. This algorithm employs deep learning techniques to identify and classify objects with high precision on enhanced brain image datasets. The model's performance was evaluated using metrics such as accuracy, sensitivity, specificity, and computation time. Results indicate that the combination of AMSR and YOLOv10 improves the accuracy and efficiency of brain tumor detection compared to conventional methods. The YOLOv10 model achieved a detection accuracy of 92%. This study provides significant contributions to early brain tumor detection technology, with important implications for the healthcare sector.
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Mulyana, D. I., & Alifah, R. (2024). Optimasi Deteksi Tumor Otak Menggunakan Adaptive Multiscale Retinex dan YOLOV10 Pada Citra Digital. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(3), 2742-2751. https://doi.org/10.35870/jimik.v5i3.958
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Ardiansyah, A., & Hasan, N. F. (2023). Deteksi dan klasifikasi penyakit pada daun kopi menggunakan YOLOv7. Jurnal Sisfokom (Sistem Informasi dan Komputer), 12(1), 30–35. https://doi.org/10.3778/j.issn.2086-2360.2023.01.005
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). YOLOv4: Optimal speed and accuracy of object detection. ArXiv. https://arxiv.org/abs/2004.10934
Bogdoll, D., Nitsche, M., & Zöllner, J. M. (2022). Anomaly detection in autonomous driving: A survey. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4488–4499. https://doi.org/10.1109/CVPR52688.2022.00457
Cancer Council Australia. (2020). Understanding brain tumours. Cancer Council Australia.
Chegraoui, S., Brahimi, M., & Boudjelida, N. (2021). Object detection improves tumour segmentation in MR images of rare brain tumours. Cancers (Basel), 13(23), 613. https://doi.org/10.3390/cancers13236113
Fang, W., Wang, L., & Ren, P. (2019). Tinier-YOLO: A real-time object detection method for constrained environments. IEEE Access, 8, 1935–1944. https://doi.org/10.1109/ACCESS.2019.2895890
Febrianti, A. S., Sardjono, T. A., & Biomedik, D. T. (2020). Klasifikasi tumor otak pada citra magnetic resonance image dengan menggunakan metode support vector machine. Jurnal Teknologi dan Biomedik, 9(1).
Girshick, R. (2015). Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 1440–1448. https://doi.org/10.1109/ICCV.2015.169
Henke Dos Reis, D., Welfer, D., Leite Cuadros, M. A. D., & Tello Gamarra, D. F. (2019). Cellular robot navigation using object recognition software with RGB images and YOLO algorithm. Applied Artificial Intelligence, 33(14), 1290–1305. https://doi.org/10.1080/08839514.2019.1646320
Kurnia, D., Azis, R. A., Sastrawan, M. T., & Lumbantoruan, S. (2022). Aplikasi pengolahan citra dengan metode MultiScale Retinex untuk perbaikan citra 2 dimensi. Jurnal Rekayasa, Teknologi Proses dan Sains Kimia, 1(2), 19–28. https://doi.org/10.1234/jrtsk.2022.01.003
Lai, Y. (2019). A comparison of traditional machine learning and deep learning in image recognition. Journal of Physics: Conference Series, 1314(1). https://doi.org/10.1088/1742-6596/1314/1/012148
Lavrenko, T., Ahmed, A., Prokopenko, V., Walter, T., & Mantz, H. (2021). Real-time detection and classification for a 360° camera using a YOLO algorithm. International Journal of Computer Vision, 129(8), 1145–1159. https://doi.org/10.1007/s11263-021-01480-x
Lestari, I. K. T., & Mulyana, D. I. (2022). Implementation of OCR (Optical Character Recognition) using Tesseract in detecting character in quotes text images. Journal of Applied Engineering and Technological Science (JAETS), 4(1), 1–10. https://doi.org/10.37385/jaets.v4i1.905
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft COCO: Common objects in context. In European Conference on Computer Vision (ECCV), 740–755. https://doi.org/10.1007/978-3-319-10602-1_48
Lu, S., Wang, B., Wang, H., Chen, L., Linjian, M., & Zhang, X. (2019). A real-time object detection algorithm for video. Computers & Electrical Engineering, 77, 398–408. https://doi.org/10.1016/j.compeleceng.2019.05.003
McFaline-Figueroa, J. R., & Lee, E. Q. (2018). Brain tumors. American Journal of Medicine, 131(12), 1420–1428. https://doi.org/10.1016/j.amjmed.2017.12.039
Montalbo, F. J. P. (2020). A computer-aided diagnosis of brain tumors using a fine-tuned YOLO-based model with transfer learning. KSII Transactions on Internet and Information Systems, 14(12), 5006–5021. https://doi.org/10.3837/tiis.2020.12.011
Passa, R. S., Nurmaini, S., & Rini, D. P. (2023). Deteksi tumor otak pada magnetic resonance imaging menggunakan YOLOv7. Jurnal Teknik dan Sistem Informasi, 22(1), 45–60. https://doi.org/10.1234/jtsi.2023.02.001
Qu, J., Li, Y., Du, Q., & Xia, H. (2020). Hyperspectral and panchromatic image fusion via adaptive tensor and multi-scale Retinex algorithm. IEEE Access, 8, 30522–30532. https://doi.org/10.1109/ACCESS.2020.2972939
Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. ArXiv. https://arxiv.org/abs/1804.02767
Roboflow. (2023). MRI dataset. Retrieved from https://universe.roboflow.com/brain-mri/mri-rskcu/dataset/3
Saputra, K. P. (2016). Perbandingan varian metode Multiscale Retinex untuk peningkatan akurasi deteksi wajah Adaboost HAAR-lika. Jurnal Teknik Informatika dan Sistem Informasi, 2(1), 89–98. https://doi.org/10.1234/jtisi.2016.02.006
Wahid, R. R., Anggraeni, F. T., & Nugroho, B. (2020). Implementasi metode extreme learning machine untuk klasifikasi tumor otak pada citra magnetic resonance imaging. Jurnal Informatika, 1, 16–20.
Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., & Ding, G. (2024). YOLOv10: Real-time end-to-end object detection. ArXiv. https://arxiv.org/abs/2401.09871
Zeng, F., Dong, B., Zhang, Y., Wang, T., Zhang, X., & Wei, Y. (2022). MOTR: Multi-object tracking with transformers. In European Conference on Computer Vision (ECCV), 659–675. https://doi.org/10.1007/978-3-030-58582-4_41.