CFP last date
20 October 2026
Call for Paper
November Edition
IJCA solicits high quality original research papers for the upcoming November edition of the journal. The last date of research paper submission is 20 October 2026

Submit your paper
Know more
The week's pick
Responsive image
Contextual Sentiment Analysis of Airbnb Customer Reviews in Albania: An Investigation of BERT-based Classification

Blerina Zanaj Serxhio Kurti Elma Zanaj
Random Articles
Reseach Article

PCOSense: A Multimodal AI Framework for PCOS (PMOS) Prediction using Clinical Data and Ultrasound Images

by Fatima Khan Sarguroh, Srivaramangai Ramanujam
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 133
Year of Publication: 2026
Authors: Fatima Khan Sarguroh, Srivaramangai Ramanujam
10.5120/ijcaa96d9ab45816

Fatima Khan Sarguroh, Srivaramangai Ramanujam . PCOSense: A Multimodal AI Framework for PCOS (PMOS) Prediction using Clinical Data and Ultrasound Images. International Journal of Computer Applications. 187, 133 ( Aug 2026), 54-63. DOI=10.5120/ijcaa96d9ab45816

@article{ 10.5120/ijcaa96d9ab45816,
author = { Fatima Khan Sarguroh, Srivaramangai Ramanujam },
title = { PCOSense: A Multimodal AI Framework for PCOS (PMOS) Prediction using Clinical Data and Ultrasound Images },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2026 },
volume = { 187 },
number = { 133 },
month = { Aug },
year = { 2026 },
issn = { 0975-8887 },
pages = { 54-63 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number133/pcosense-a-multimodal-ai-framework-for-pcos-pmos-prediction-using-clinical-data-and-ultrasound-images/ },
doi = { 10.5120/ijcaa96d9ab45816 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-08-20T21:54:41.495230+05:30
%A Fatima Khan Sarguroh
%A Srivaramangai Ramanujam
%T PCOSense: A Multimodal AI Framework for PCOS (PMOS) Prediction using Clinical Data and Ultrasound Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 133
%P 54-63
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Polycystic Ovary Syndrome (PCOS) is one of the most common hormonal disorders seen in women of reproductive age. Recently, PCOS has been renamed as Polyendocrine Metabolic Ovarian Syndrome (PMOS). In this research, a multimodal AI framework called PCOSense is proposed that can predict PCOS using clinical data and ovarian ultrasound images. For clinical prediction, a hybrid machine learning algorithm was applied. To classify ultrasound images for PCOS diagnosis, a deep learning algorithm that was built on MobileNetV2 with an attention and residual learning scheme was implemented. To enhance the accuracy level of the diagnosis, the probability outputs from both models were fused at the decision level to make a final prediction. According to the experimental results, it can be seen that the performance of the classifier for PCOS classification from ultrasound image is high. Moreover, a web application was designed based on the Streamlit framework to predict PCOS in real time using clinical variables and ultrasound images. This shows the practical usefulness of the proposed framework for intelligent healthcare support.

References
  1. K. Choudhary, R. Singh, A. Garg, N. Verma, A. Purohit, and D. Deora, “An Updated Overview of Polycystic Ovary Syndrome,” Innovare Journal of Medical Sciences, vol. 7, no. 3, pp. 1–13, 2019.
  2. P. Bhandari, “Polycystic Ovary Syndrome (PCOS): Causes, Symptoms, Diagnosis, and Treatment,” The American Journal of Patient Health Info, vol. 2, no. 2, 2025. DOI: https://doi.org/10.69512/ajphi.v2i2.83
  3. L. Shahmoradi, L. Azadbakht, J. Farzi, S. R. N. Kalhori, A. B. Yazdipour, and F. Solat, “Nutritional management recommendation systems in polycystic ovary syndrome: a systematic review,” BMC Women’s Health, vol. 24, no. 1, p. 234, 2024. DOI: https://doi.org/10.1186/s12905-024-03074-3
  4. S. Rajendran, R. Bama, and K. Maheswari, “A deep learning and machine learning-based hybrid approach for PCOS diagnosis using ultrasound images,” Scientific Reports, vol. 12, no. 1, p. 18141, 2022. DOI: https://doi.org/10.1038/s41598-022-18141-0
  5. S. J. Pulluparambil and S. Bhat, “Medical image processing: Detection and prediction of PCOS—a systematic literature review,” International Journal of Health Sciences and Pharmacy (IJHSP), vol. 5, no. 2, pp. 80–98, 2021. DOI: https://doi.org/10.47992/IJHSP.2581.6411.0075
  6. M. Priyadharshini, A. Srimathi, C. Sanjay, and K. Ramprakash, “PCOS disease prediction using machine learning algorithms,” International Research Journal of Advanced Engineering Hub (IRJAEH), vol. 2, no. 3, pp. 651–655, 2024. DOI: https://doi.org/10.47392/IRJAEH.2024.0094
  7. B. Yamini, V. R. Kaneti, M. Nalini, and S. Subramanian, “Machine learning-driven PCOS prediction for early detection and tailored interventions,” SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 9, pp. 61–75, 2023. DOI: https://doi.org/10.14445/23488379/IJEEE-V10I9P106
  8. M. M. Rahman et al., “Empowering early detection: A web-based machine learning approach for PCOS prediction,” Informatics in Medicine Unlocked, vol. 47, p. 101500, 2024. DOI: https://doi.org/10.1016/j.imu.2024.101500
  9. Z. Zad et al., “Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records,” Frontiers in Endocrinology, vol. 15, p. 1298628, 2024. DOI: https://doi.org/10.3389/fendo.2024.1298628
  10. B. Panjwani, J. Yadav, V. Mohan, N. Agarwal, and S. Agarwal, “Optimized machine learning for the early detection of polycystic ovary syndrome in women,” Sensors, vol. 25, no. 4, p. 1166, 2025. DOI: https://doi.org/10.3390/s25041166
  11. H. Elmannai et al., “Polycystic ovary syndrome detection machine learning model based on optimized feature selection and explainable artificial intelligence,” Diagnostics, vol. 13, no. 8, p. 1506, 2023. DOI: https://doi.org/10.3390/diagnostics13081506
  12. V. Sakthivel, P. Prakash, K. Vishnukumar, and D. Min, “Advanced diagnosis of polycystic ovarian syndrome using machine learning and multimodal data integration,” International Journal of Advanced Computer Science and Applications, vol. 15, no. 6, 2024. DOI: https://dx.doi.org/10.14569/IJACSA.2024.01506122
  13. S. A. Suha and M. N. Islam, “An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image,” Scientific Reports, vol. 12, no. 1, p. 17123, 2022. DOI: https://doi.org/10.1038/s41598-022-21724-0
  14. M. Sumathi, P. Chitra, R. S. Prabha, and K. Srilatha, “Study and detection of PCOS related diseases using CNN,” in IOP Conference Series: Materials Science and Engineering, vol. 1070, no. 1, p. 012062, Feb. 2021. DOI: https://doi.org/10.1088/1757-899X/1070/1/012062
  15. S. Ahmed et al., “A review on the detection techniques of polycystic ovary syndrome using machine learning,” IEEE Access, vol. 11, pp. 86522–86543, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3304536
  16. D. Rao, R. R. Dayma, S. K. Pendekanti, and A. K. Acharya, “Deep learning model for diagnosing polycystic ovary syndrome using a comprehensive dataset from Kerala hospitals,” International Journal of Electrical and Computer Engineering, vol. 14, no. 5, pp. 5715–5727, 2024. DOI: https://doi.org/10.11591/ijece.v14i5.pp5715-5727
  17. H. Pushkarini and M. A. Anusuya, “A prediction model for evaluating the risk of developing PCOS,” International Research Journal of Engineering and Technology (IRJET), vol. 7, no. 9, pp. 1150–1156, 2020. DOI: https://www.irjet.net/archives/V7/i9/IRJET-V7I9192.pdf
  18. F. J. Barrera et al., “Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: A systematic review,” Frontiers in Endocrinology, vol. 14, p. 1106625, 2023. DOI: https://doi.org/10.3389/fendo.2023.1106625
  19. T. Arunprasath, R. Ramalakshmi, R. Kottaimalai, and J. Alex Michael Raj, “Development of a machine learning model to classify polycystic ovarian syndrome,” Technology and Health Care, vol. 33, no. 3, pp. 1478–1488, 2025. DOI: https://doi.org/10.1177/09287329241296357
  20. R. Galagan, S. Andreiev, N. Stelmakh, Y. Rafalska, and A. Momot, “Automation of polycystic ovary syndrome diagnostics through machine learning algorithms in ultrasound imaging,” Applied Computer Science, vol. 20, no. 2, pp. 194–204, 2024. DOI: https://doi.org/10.35784/acs-2024-24
  21. R. Ahmad, L. A. Maghrabi, I. A. Khaja, L. A. Maghrabi, and M. Ahmad, “SMOTE-Based Automated PCOS Prediction Using Lightweight Deep Learning Models,” Diagnostics, vol. 14, no. 19, p. 2225, 2024. DOI: https://doi.org/10.3390/diagnostics14192225
  22. R. Mogos et al., “Predicting unfavorable pregnancy outcomes in polycystic ovary syndrome (PCOS) patients using machine learning algorithms,” Medicina, vol. 60, no. 8, p. 1298, 2024. DOI: https://doi.org/10.3390/medicina60081298
  23. N. Leslie, A. A. Permana, and A. T. Perdana, “Application of the K-Nearest Neighbor Algorithm for Polycystic Ovarian Syndrome (PCOS) Classification: A Diagnostic Tool,” Journal of Logistics, Informatics and Service Science, vol. 11, no. 10, 2024. DOI: https://doi.org/10.33168/JLISS.2024.1011
  24. K. P. Rakshitha and N. C. Naveen, “Op-RMSprop classification for prediction of polycystic ovary syndrome using hybrid machine learning technique,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 6, 2022. DOI:https://dx.doi.org/10.14569/IJACSA.2022.0130671
  25. C. Khadse and C. Puri, “Early detection of polycystic ovary syndrome using a hybrid machine learning model,” International Journal of Advanced Multidisciplinary Research and Educational Development, vol. 1, no. 4, pp. 300–318, 2025. DOI: https://doi.org/10.1109/IDICAIHEI65991.2025.11379286
  26. S. Venkatalakshmi and M. Regina, “Leveraging hybrid machine learning models for early diagnosis and prediction of Polycystic Ovary Syndrome (PCOS),” Journal of Electrical Systems, vol. 20, no. 11S, pp. 1765–1775, 2024. Available: https://journal.esrgroups.org/jes/article/view/7586
  27. E. Alamoudi et al., “A deep learning fusion approach for diagnosis of Polycystic Ovary Syndrome (PCOS) using ultrasound images and clinical data,” Applied Computational Intelligence and Soft Computing, 2023. DOI: https://doi.org/10.1155/2023/9686697
  28. P. N. Sai et al., “Advanced diagnosis of Polycystic Ovary Syndrome using multimodal machine learning techniques,” International Journal of Engineering Research and Technology, 2023. DOI: https://dx.doi.org/10.14569/IJACSA.2024.01506122
  29. M. M. Hoque et al., “Vision models for medical imaging: A hybrid deep learning approach for PCOS detection using ultrasound images,” arXiv preprint, 2026. DOI: https://doi.org/10.48550/arXiv.2601.15119
  30. P. Moral, D. Mustafi, A. Mustafi, and S. K. Sahana, “CystNet: An AI-driven model for PCOS detection using multilevel thresholding of ultrasound images,” Scientific Reports, 2024. https://www.nature.com/articles/s41598-024-75964-3.pdf
  31. V. Lakshmi and B. Pushpa, “Explainable multimodal deep learning using cross-attention fusion of ultrasound and clinical features for PCOS classification,” Discover Computing, 2026. DOI: https://doi.org/10.1007/s10791-025-09901-x
  32. M. Sumathi, P. Chitra, S. Sheela, and C. Ishwarya, “Study and implementation of automated system for detection of PCOS from ultrasound scan images using artificial intelligence,” The Imaging Science Journal, 2023. DOI: https://doi.org/10.1080/13682199.2023.2229016
  33. J. Peng, Z. Guo, X. Chen, and M. Zhou, “Denoising degraded PCOS ultrasound images using an enhanced denoising diffusion probabilistic model,” Electronics, vol. 14, p. 4061, 2025. DOI: https://doi.org/10.3390/electronics14194061
  34. F. K. Sarguroh and R. Srivaramangai, “An analytical review of AI-driven techniques for Polycystic Ovary Syndrome prediction,” International Journal of Science and Research (IJSR), Dec. 2025. DOI: https://dx.doi.org/10.21275/SR251217131307.
  35. F. K. Sarguroh and S. Ramanujam, “Hybrid Machine Learning Framework for Clinical Diagnosis of Polycystic Ovary Syndrome,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2026. DOI: https://doi.org/10.35940/ijitee.A8344.15040326
Index Terms

Computer Science
Information Sciences

Keywords

Polycystic Ovarian Syndrome (PCOS) Multimodal Framework Artificial Intelligence (AI) Machine Learning (ML) PMOS Deep Learning (DL) Ultrasound Images Fusion Prediction

Powered by PhDFocusTM