ArticleScientific reports2022
An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 4 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 4 syntheses or guidelines pooled it, 140 citations in OpenAlex.
- Machine learning and deep learning for diagnosis of Polyendocrine Metabolic Ovarian Syndrome: systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Artificial intelligence in polycystic ovary syndrome: a systematic review of diagnostic and predictive applications.BMC medical informatics and decision making · 2025Pooled it
- Artificial intelligence in polycystic ovarian syndrome management: past, present, and future.La Radiologia medica · 2025Pooled it
- Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2025Pooled it
- Construction and validation of a machine learning model for predicting early pregnancy in patients with polycystic ovary syndrome: a retrospective cohort study.Journal of ovarian research · 2026Article
- Diagnostic performance of deep learning models on ultrasound images for distinguishing benign from malignant ovarian cysts.Journal of ovarian research · 2026Article
- A multimodal feature fusion with deep representation learning approach for polycystic ovary syndrome diagnosis using ultrasound images.Scientific reports · 2026Article
- AutoPCOS: a stepwise multimodal intelligent framework for polycystic ovary syndrome risk stratification and diagnostic support.Frontiers in endocrinology · 2026Article
- Article
- Non-Invasive and AI-Enabled Diagnostic Approaches for Polycystic Ovary Syndrome: A PRISMA-Guided Narrative Review.International journal of women's health · 2026Review
- Article
- Ensemble deep learning with advanced feature engineering for embryo evaluation on in-vitro fertilisation procedures using biomedical images.Scientific reports · 2025Article
- Feature fusion context attention gate UNet for detection of polycystic ovary syndrome.Scientific reports · 2025Article
- A Deep Learning-Based Automatic Recognition Model for Polycystic Ovary Ultrasound ImagesBalkan medical journal · 2025Article
- Detecting heavy trucks from mobile phone trajectories using image-based behavioral representations and deep learning models.Scientific reports · 2025Article
- Harnessing Microbiome, Bacterial Extracellular Vesicle, and Artificial Intelligence for Polycystic Ovary Syndrome Diagnosis and Management.Biomolecules · 2025Review
- Advanced holographic convolutional dense networks and Tangent runner optimization for enhanced polycystic ovarian disease classification.Scientific reports · 2025Article
- The Role of Artificial Intelligence in Female Infertility Diagnosis: An Update.Journal of clinical medicine · 2025Review
- Polycystic ovary syndrome as a metabolic disease.Nature reviews. Endocrinology · 2025Review
- Ultrasound Assessment in Polycystic Ovary Syndrome Diagnosis: From Origins to Future Perspectives-A Comprehensive Review.Biomedicines · 2025Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality and one of the primary causes of anovulatory infertility in women globally. The detection of multiple cysts using ovary ultrasonograpgy (USG) scans is one of the most reliable approach for making an accurate diagnosis of PCOS and creating an appropriate treatment plan to heal the patients with this syndrome. Instead of depending on error-prone manual identification, an intelligent computer-aided cyst detection system can be a viable approach. Therefore, in this research, an extended machine learning classification technique for PCOS prediction has been proposed, trained and tested over 594 ovary USG images; where the Convolutional Neural Network (CNN) incorporating different state-of-the-art techniques and transfer learning has been employed for feature extraction from the images; and then stacking ensemble machine learning technique using conventional models as base learners and bagging or boosting ensemble model as meta-learner have been used on that reduced feature set to classify between PCOS and non-PCOS ovaries. The proposed technique significantly enhances the accuracy while also reducing training execution time comparing with the other existing ML based techniques. Again, following the proposed extended technique, the best performing results are obtained by incorporating the "VGGNet16" pre-trained model with CNN architecture as feature extractor and then stacking ensemble model with the meta-learner being "XGBoost" model as image classifier with an accuracy of 99.89% for classification.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.