Utilization of YOLO for Detection and Segmentation of Ultrasound Medical Images in Hydatidiform Mole (Molar Pregnancy)
DOI:
https://doi.org/10.62205/mjgcs.v3i2.43Keywords:
YOLO, ultrasound, hydatidiform mole detection, medical imaging, accuracy, efficiencyAbstract
This study aims to explore the utilization of the YOLO (You Only Look Once) algorithm for detecting and segmenting ultrasound (USG) medical images in cases of hydatidiform mole (molar pregnancy). Hydatidiform mole is a pathological condition that requires rapid and accurate diagnosis to prevent serious complications. By using a diverse dataset of ultrasound images, the YOLO model was trained to recognize and identify suspicious areas within the images. Evaluation results indicate that this model significantly improves detection and segmentation accuracy compared to manual analysis methods, while also providing higher efficiency in the diagnostic process. The implementation of a YOLO-based system is expected to assist healthcare professionals in making quicker and more accurate decisions, thereby enhancing health outcomes for patients. This research highlights the potential of artificial intelligence technology in the medical field, particularly in diagnosing complex obstetric conditions.
References
Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436-444, 2015. DOI: 10.1038/nature14539
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017. DOI: 10.1145/3065386
G. Litjens, T. Kooi, B. E. Bejnordi, et al., “A Survey on Deep Learning in Medical Image Analysis,” Medical Image Analysis, vol. 42, pp. 60-88, 2017. DOI: 10.1016/j.media.2017.07.005
D. Shen, G. Wu, and H.-I. Suk, “Deep Learning in Medical Image Analysis,” Annual Review of Biomedical Engineering, vol. 19, pp. 221-248, 2017. DOI: 10.1146/annurev-bioeng-071516-044442
S. Liu, Y. Wang, X. Yang, et al., “Deep Learning in Medical Ultrasound Analysis: A Review,” Engineering, vol. 5, no. 2, pp. 261-275, 2019. DOI: 10.1016/j.eng.2018.11.020
Y. Li, H. Gu, H. Wang, and P. Qin, “BUSnet: A Deep Learning Model of Breast Tumor Lesion Detection for Ultrasound Images,” Frontiers in Oncology, vol. 12, art. 848271, 2022. DOI: 10.3389/fonc.2022.848271
O. A. Ajilisa, V. P. Jagathyraj, and M. K. Sabu, “Computer-Aided Diagnosis of Thyroid Nodule from Ultrasound Images Using Transfer Learning from Deep Convolutional Neural Network Models,” in Proc. 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), 2020, pp. 237-241. DOI: 10.1109/ACCTHPA49271.2020.9213210
R. Girshick, “Fast R-CNN,” in Proc. 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1440-1448. DOI: 10.1109/ICCV.2015.169
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017. DOI: 10.1109/TPAMI.2016.2577031
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779-788. DOI: 10.1109/CVPR.2016.91
J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” in Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 6517-6525. DOI: 10.1109/CVPR.2017.690
J. Terven, D.-M. Córdova-Esparza, and J.-A. Romero-González, “A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS,” Machine Learning and Knowledge Extraction, vol. 5, no. 4, pp. 1680-1716, 2023. DOI: 10.3390/make5040083
S. Tan, G. Lu, Z. Jiang, and L. Huang, “Improved YOLOv5 Network Model and Application in Safety Helmet Detection,” in Proc. 2021 IEEE International Conference on Intelligence and Safety for Robotics (ISR), 2021, pp. 330-333. DOI: 10.1109/ISR50024.2021.9419561
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI), LNCS vol. 9351, 2015, pp. 234-241. DOI: 10.1007/978-3-319-24574-4_28
M. Sokolova and G. Lapalme, “A Systematic Analysis of Performance Measures for Classification Tasks,” Information Processing & Management, vol. 45, no. 4, pp. 427-437, 2009. DOI: 10.1016/j.ipm.2009.03.002
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