DETEKSI TUMOR OTAK PADA MAGNETIC RESONANCE IMAGING MENGGUNAKAN YOLOv7

Rahma Satila Passa; Siti Nurmaini; Dian Palupi Rini

doi:10.33557/jurnalmatrik.v25i2.2404

Rahma Satila Passa Universitas Sriwijaya
Siti Nurmaini Universitas Sriwijaya
Dian Palupi Rini Universitas Sriwijaya

DOI: https://doi.org/10.33557/jurnalmatrik.v25i2.2404

Keywords: object detection, brain tumor, yolov7

Abstract

Abstract : Brain tumor detection is an important task in medical image analysis and diagnosis. In this study, we propose a brain tumor detection model based on deep learning with YOLOv7 which can accurately detect brain tumors. The dataset used is divided into training and testing datasets. Pre-processing techniques are carried out on the dataset to get maximum results. Research produces a detection model for brain tumors. The model achieved a mAP of 93.2%, precision of 91.4%, recall of 90.2% and F1-score of 90.8%. The results demonstrate the effectiveness of the proposed model in accurately detecting brain tumors, which can assist early diagnosis and treatment planning.

Abstrak : Deteksi tumor otak adalah tugas penting dalam analisis dan diagnosa citra medis. Dalam penelitian ini, kami mengusulkan model deteksi tumor otak berdasarkan deep learning dengan YOLOv7 yang dapat mendeteksi tumor otak secara akurat. Dataset yang digunakan terbagi menjadi dataset pelatihan dan pengujian. Teknik pra proses dilakukan pada dataset untuk mendapatkan hasil yang maksimal. Penelitian menghasilkan model deteksi untuk tumor otak. Model mencapai mAP 93,2%, precision 91,4%, recall 90,4%, dan F1-score 90,8%. Hasilnya menunjukkan keefektifan model yang diusulkan dalam mendeteksi tumor otak secara akurat, yang dapat membantu diagnosis dini dan perencanaan perawatan.

References

S. Nurmaini et al., “Deep learning‐based computer‐aided fetal echocardiography: Application to heart standard view segmentation for congenital heart defects detection,” Sensors, vol. 21, no. 23, 2021, doi: 10.3390/s21238007.

M. Arnaldo, S. Nurmaini, H. Satria, and M. N. Rachmatullah, “Multiclass Segmentation of Pulmonary Diseases using Convolutional Neural Network,” Comput. Eng. Appl. J., vol. 11, no. 1, pp. 63–71, 2022, doi: 10.18495/comengapp.v11i1.397.

A. W. Arum, S. Nurmaini, D. P. Rini, P. Agustiansyah, and M. N. Rachmatullah, “Segmentation of Squamous Columnar Junction on VIA Images using U-Net Architecture,” Comput. Eng. Appl. J., vol. 10, no. 3, pp. 209–219, 2021, doi: 10.18495/comengapp.v10i3.387.

N. Abiwinanda, M. Hanif, S. T. Hesaputra, A. Handayani, and T. R. Mengko, “Brain tumor classification using convolutional neural network,” 2019, doi: 10.1007/978-981-10-9035-6_33.

J. R. McFaline-Figueroa and E. Q. Lee, “Brain Tumors,” American Journal of Medicine. 2018, doi: 10.1016/j.amjmed.2017.12.039.

B. Khagi and G. R. Kwon, “3D CNN based alzheimer’s diseases classification using segmented grey matter extracted from whole-brain MRI,” Int. J. Informatics Vis., 2021, doi: 10.30630/joiv.5.2.572.

F. Ali et al., “A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI,” Comput. Mater. Contin., 2022, doi: 10.32604/cmc.2022.024103.

F. J. P. Montalbo, “A computer-aided diagnosis of brain tumors using a fine-tuned yolo-based model with transfer learning,” KSII Trans. Internet Inf. Syst., 2020, doi: 10.3837/tiis.2020.12.011.

H. Chegraoui et al., “Object detection improves tumour segmentation in mr images of rare brain tumours,” Cancers (Basel)., 2021, doi: 10.3390/cancers13236113.

T. Lavrenko, A. Ahmed, V. Prokopenko, T. Walter, and H. Mantz, “Real-Time Detection and Classification for a 360 ° -Camera Using a YOLO Algorithm,” 2021.

C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” pp. 1–15, 2022, [Online]. Available: http://arxiv.org/abs/2207.02696.

Y. Xia, M. Nguyen, and W. Q. Yan, “A Real-time Kiwifruit Detection Based on Improved YOLOv7,” Int. Conf. Image Vis. Comput. New Zeal., pp. 48–61, 2023, doi: 10.1007/978-3-031-25825-1_4.

Z. Yang, “A Yolov7 Based Visual Detection of Waste,” pp. 80–85, 2022.

J. Cheng, “Brain tumor dataset,” Figshare, 2017.

J. Cheng et al., “Enhanced performance of brain tumor classification via tumor region augmentation and partition,” PLoS One, 2015, doi: 10.1371/journal.pone.0140381.

D. Wu et al., “Detection of Camellia oleifera Fruit in Complex Scenes by Using YOLOv7 and Data Augmentation,” Appl. Sci., vol. 12, no. 22, p. 11318, 2022, doi: 10.3390/app122211318.

S. Pan, J. Liu, and D. Chen, “Research on License Plate Detection and Recognition System based on YOLOv7 and LPRNet,” vol. 4, no. 2, pp. 62–68, 2022.