Improvement of Ovarian Detection from Ultrasound Images Using Ensemble Learning-Reference-Cited by-同舟云学术

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Improvement of Ovarian Detection from Ultrasound Images Using Ensemble Learning

Published:2024 Issue: Volume: Page:472-478
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:en
Short-container-title:

Author:

Ngo Van-Anh,Ta Nam-Anh,Pham Thi-Loan,Le Van-Hung,Vu Duy-Hai,Le Thi-Lan,Vu Hai,Tran Thanh-Hai

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-97-5504-2_56

Reference15 articles.

1. Alamoudi, A., et al.: A deep learning fusion approach to diagnosis the polycystic ovary syndrome (pcos). Appl. Comput. Intell. Soft Comput. 2023 (2023)

2. Bansal, C., et al.: Comparing different models for polycystic ovary syndrome diagnosis: an empirical investigation on a large clinical dataset. In: 2023 IEEE Women in Technology Conference, pp. 1–6 (2023)

3. Chen, X., et al.: Microsoft coco captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325 (2015)

4. Chen, Y.N., Ma, F., Zhang, Y.D., Chen, L., Li, C.Y., Gong, S.P.: Ultrasound features improve diagnostic performance of ovarian cancer predictors in distinguishing benign and malignant ovarian tumors. Curr. Med. Sci. 40, 184–191 (2020)

5. Wanderley, D., Ferreira, C., Campilho, A., Silva, J.: Ovarian structures detection using convolutional neural networks. Procedia Comput. Sci. 196, 542–549 (2022)

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1. A Method for Ovarian Tumor Segmentation based on Segment Anything Model;2024 International Conference on Multimedia Analysis and Pattern Recognition (MAPR);2024-08-15

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