ArticleJournal of medical systems2019
Classification and Recognition of Ovarian Cells Based on Two-Dimensional Light Scattering Technology.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Electrochemical-assisted scattering imaging system for lymphoma cell classification using machine learning.Biomedical optics express · 2025Article
- The Evolving Landscape of Ovarian Cancer: Innovations in Biotechnology and Artificial Intelligence- Based Screening and Treatment.Current treatment options in oncology · 2025Review
- RETRACTED: Gynecological Healthcare: Unveiling Pelvic Masses Classification through Evolutionary Gravitational Neocognitron Neural Network Optimized with Nomadic People Optimizer.Diagnostics (Basel, Switzerland) · 2023Article
- Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery.Cancer biomarkers : section A of Disease markers · 2022Review
- Progress of Artificial Intelligence in Gynecological Malignant Tumors.Cancer management and research · 2020Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian cancer is a very insidious malignant tumor. In order to detect ovarian cancer cells early, the classification and recognition of ovarian cancer cells is mainly studied by two-dimensional light scattering technology. Firstly, a single-cell two-dimensional light scattering pattern acquisition platform based on single-mode optical fiber illumination is designed to collect a certain number of two-dimensional light scattering patterns of ovarian cancer cells and normal ovarian cells. Then, the HOG (Histogram of Oriented Gradient) algorithm is used to extract shaving anisotropy feature of two-dimensional light scattering pattern. The results show that the accuracy of classification and identification of ovarian cancer cells by two-dimensional light scattering technology is 90.81%, which suggests that the specificity of cancer cells and normal cells can be characterized by two-dimensional light scattering technology.
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Registered trials
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