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Detection of polycystic ovarian syndrome using follicle recognition technique

  • Published:2021-11 Issue:2 Volume:2 Page:304-308
  • ISSN:2666-285X
  • Container-title:Global Transitions Proceedings
  • language:en
  • Short-container-title:Global Transitions Proceedings

Author:

Rachana B,Priyanka T,Sahana K N,Supritha T R,Parameshachari B D,Sunitha R

Publisher

Elsevier BV

Reference23 articles.

1. Chandrul A Review on “Polycystic Overy Syndrome (PCOS);Morang;Int. J. Trend Sci. Res. Dev. (IJTSRD),2019

2. Image Enhancement using Histogram Equalization;Raghavendra,2020

3. Comparative Study on “Histogram Equalization Techniques for Medical Image Enhancement”;Sakshi,2020

4. “Machine learning based ovarian detection in ultrasound images”;Kiruthika;Int. J. Adv. Mechatronic Syst.,2020

5. “Image Processing based on Adaptive Morphological Techniques”;Ajay Kumar,2019

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1. Nanotechnology in the diagnosis and management of polycystic ovary syndrome: A systematic scoping analysis to improve patient care;Next Research;2024-10

2. Polycystic ovary syndrome detection using optimized SVM and DenseNet;International Journal of Information Technology;2024-09-04

3. F-Net: Follicles Net an efficient tool for the diagnosis of polycystic ovarian syndrome using deep learning techniques;PLOS ONE;2024-08-15

4. Leveraging Machine Learning to Detect and Predict Diabetes in Polycystic Ovary Syndrome Patients: A Review;2024 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD);2024-08-01

5. Polycystic Ovary Syndromedetection via Hybrid Model with Improved Resampling Method;2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS);2024-06-28
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