Performance Analysis of Various Filters for Denoising Breast Cancer Histopathology Images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Obstetrics and Gynecology,Oncology
Link
https://link.springer.com/content/pdf/10.1007/s40944-023-00761-1.pdf
Reference27 articles.
1. Krithiga R, Geetha P. Breast cancer detection, segmentation and classification on histopathology images analysis: a systematic review. Arch Comput Methods Eng. 2021;28:2607–19. https://doi.org/10.1007/s11831-020-09470-w.
2. Sohail A, Khan A, Nisar H, Tabassum S, Zameer A. Mitotic nuclei analysis in breast cancer histopathology images using deep ensemble classifier. Med Image Anal. 2021;72:102121. https://doi.org/10.1016/j.media.2021.102121.
3. Kleczek P, Jaworek-Korjakowska J, Gorgon M. A novel method for tissue segmentation in high-resolution H&E-stained histopathological whole-slide images. Comput Med Imaging Graph. 2020;79:101686. https://doi.org/10.1016/j.compmedimag.2019.101686.
4. Ramesh G, Logeshwaran J, Gowri J, Mathew A. The management and reduction of digital noise in video image processing by using transmission based noise elimination scheme. ICTACT J Image Video Process. 2022;13:2797–801. https://doi.org/10.21917/ijivp.2022.0398.
5. Sigirci IO, Albayrak A, Bilgin G. Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features. Multimedia Tools Appl. 2022;81:13179–202. https://doi.org/10.1007/s11042-021-10539-2.
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