Detection of cervical cancer cells in complex situation based on improved YOLOv3 network

Author:

Jia Dongyao,He Zihao,Zhang ChuanwangORCID,Yin Wanting,Wu Nengkai,Li Ziqi

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference57 articles.

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3. Arthur D, Vassilvitskii S (2007) K-Means++: The Advantages of Careful Seeding[C]// Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, New Orleans, Louisiana, USA, January 7-9, 2007. ACM

4. Arya M, Mittal N, Singh G (2018) Texture-based feature extraction of smear images for the detection of cervical cancer[J]. IET Comput Vis 12(8):1049–1059

5. Basheer S, Mariyam Aysha Bivi S, Jayakumar S et al (2019) Machine learning based classification of cervical cancer using K-nearest neighbour, random forest and multilayer perceptron algorithms[J]. J Comput Theor Nanosci 16(5–6):2523–2527

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