A Hybrid Deep Learning Framework for Accurate Polycystic Ovary Syndrome Detection Using Ultrasound Imaging

Authors

A. Boobalan
Galgotias University image/svg+xml
P. Sudhakar
Galgotias University image/svg+xml
DOI: https://doi.org/10.52549/ijeei.v13i3.6565
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Published: 13-09-2025 • Issue: Vol. 13 No. 3: September 2025 • Section: Regular paper

Abstract

Polycystic Ovarian Syndrome (PCOS) is a hormone-related health condition in women, commonly classified as an endocrine disorder. It is most prevalent during the childbearing years, typically between the ages of 15 and 44. PCOS leads to hormonal imbalances that cause irregular menstrual cycles, hair loss, and other symptoms, and it is associated with long-term health risks such as heart disease and diabetes. Recent advances in deep learning have shown promising results in accurately recognizing and differentiating ovarian cysts from other ovarian tumours. This study proposes a novel technique for PCOS symptom detection by analysing ovarian images through feature extraction, classification, and metaheuristic-based optimization. Ovarian images are first pre-processed for noise removal and smoothing, followed by feature extraction and classification using a Convolutional Wavelet Attention Neural Network with a Naïve Bayes Fuzzy Autoencoder (CWANN–NBFA). Optimization is then performed using the Metaheuristic Multilevel Hawks Algae Optimization (MMHAO) algorithm. Experimental evaluations were conducted on multiple ovarian image datasets. The proposed technique achieved an accuracy of over 98% across the PCOSUSG, KFHU, and MMOTU datasets, demonstrating its robustness and effectiveness in addressing the challenges of PCOS detection.
Keywords: PCOS symptoms detection; Ovary image; Ensemble deep learning; Metaheuristic optimization; Feature extraction

References

C. Gopalakrishnan and M. Iyapparaja, “Multilevel thresholding based follicle detection and classification of polycystic ovary syndrome from the ultrasound images using machine learning,†International Journal of System Assurance Engineering and Management, 2021, doi: 10.1007/s13198-021-01203-x.

S. Srivastava, P. Kumar, V. Chaudhry, and A. Singh, “Detection of Ovarian Cyst in Ultrasound Images Using Fine-Tuned VGG-16 Deep Learning Network,†SN Comput Sci, vol. 1, no. 2, p. 81, 2020, doi: 10.1007/s42979-020-0109-6.

Y. Suganya, S. Ganesan, P. Valarmathi, and T. Suresh, “A diagnosis of ovarian cyst using deep learning neural network with XGBoost algorithm,†International Journal of Information Technology, vol. 15, no. 7, pp. 3499–3506, 2023, doi: 10.1007/s41870-023-01401-9.

K. Prabu and P. Sudhakar, “A hybrid deep learning approach for enhanced network intrusion detection,†Indonesian Journal of Electrical Engineering and Computer Science, vol. 33, no. 3, p. 1915, Mar. 2024, doi: 10.11591/ijeecs.v33.i3.pp1915-1923.

S. E. Morsy, N. M. Abd-Elsalam, Z. A. Al-Saidy, A. H. Kandil, A. M. El-Bialy, and A. B. M. Youssef, “A model for classifying breast masses in ultrasound images,†International Journal of Advances in Applied Sciences, vol. 13, no. 3, pp. 566–578, Sep. 2024, doi: 10.11591/ijaas.v13.i3.pp566-578.

Z. Zhang and Y. Han, “Detection of Ovarian Tumors in Obstetric Ultrasound Imaging Using Logistic Regression Classifier with an Advanced Machine Learning Approach,†IEEE Access, vol. 8, pp. 44999–45008, 2020, doi: 10.1109/ACCESS.2020.2977962.

D. B. Unggul, N. Iriawan, and H. Kuswanto, “Transfer learning scenarios on deep learning for ultrasound-based image segmentation,†IAES International Journal of Artificial Intelligence, vol. 13, no. 3, pp. 3273–3282, Sep. 2024, doi: 10.11591/ijai.v13.i3.pp3273-3282.

V. Sheeja Kumari, T. Manikandan, A. Selva Kumar, S. Ponmaniraj, and K. Prabu, Using Improved Finite Element Modeling to Combat Cardiovascular Disease: A Review of a Developing Area at the Intersection of Several Disciplines. 2024. doi: 10.1201/9781003469612-8.

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, Oct. 2024, doi: 10.11591/ijece.v14i5.pp5715-5727.

Y. Du, W. Guo, Y. Xiao, H. Chen, J. Yao, and J. Wu, “Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study,†BMC Med Imaging, vol. 24, no. 1, Dec. 2024, doi: 10.1186/s12880-024-01251-2.

T. N. Ravishankar, H. Makarand Jadhav, N. Satheesh Kumar, S. Ambala, and M. Pillai N, “A deep learning approach for ovarian cysts detection and classification (OCD-FCNN) using fuzzy convolutional neural network,†Measurement: Sensors, vol. 27, Jun. 2023, doi: 10.1016/j.measen.2023.100797.

V. Kiruthika, S. Sathiya, M. M. Ramya, and K. S. Sankaran, “An Intelligent Machine Learning Approach for Ovarian Detection and Classification System using Ultrasonogram Images,†Engineered Science, vol. 23, Jun. 2023, doi: 10.30919/es8d879.

C. A. Graham et al., “A practical evaluation of machine learning for classification of ultrasound images of ovarian development in channel catfish (Ictalurus punctatus),†Aquaculture, vol. 552, p. 738039, 2022, doi: https://doi.org/10.1016/j.aquaculture.2022.738039.

S. Bharati, P. Podder, and M. R. Hossain Mondal, “Diagnosis of Polycystic Ovary Syndrome Using Machine Learning Algorithms,†in 2020 IEEE Region 10 Symposium (TENSYMP), 2020, pp. 1486–1489. doi: 10.1109/TENSYMP50017.2020.9230932.

S. Athithan, S. Sachi, and A. K. Singh, “Ultrasound-Based Ovarian Cysts Detection with Improved Machine-Learning Techniques and Stage Classification Using Enhanced Classifiers,†SN Comput Sci, vol. 4, no. 5, p. 571, 2023, doi: 10.1007/s42979-023-01973-0.

R. Wang et al., “Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging,†Eur Radiol, vol. 31, no. 7, pp. 4960–4971, 2021, doi: 10.1007/s00330-020-07266-x.

M. J. Sundari and N. C. Brintha, “A Comparative Study of Various Machine Learning Methods on Ovarian Tumor,†in 2021 Sixth International Conference on Image Information Processing (ICIIP), 2021, pp. 314–319. doi: 10.1109/ICIIP53038.2021.9702697.

S. K. Prabhakar and S. W. Lee, “An Integrated Approach for Ovarian Cancer Classification with the Application of Stochastic Optimization,†IEEE Access, vol. 8, pp. 127866–127882, 2020, doi: 10.1109/ACCESS.2020.3006154.

S. Sreejith, H. Khanna Nehemiah, and A. Kannan, “A clinical decision support system for polycystic ovarian syndrome using red deer algorithm and random forest classifier,†Healthcare Analytics, vol. 2, Nov. 2022, doi: 10.1016/j.health.2022.100102.

I. S. Rajput, S. Tyagi, A. Gupta, and V. Jain, “Sine cosine algorithm-based feature selection for improved machine learning models in polycystic ovary syndrome diagnosis,†Multimed Tools Appl, vol. 83, no. 30, pp. 75007–75031, 2024, doi: 10.1007/s11042-024-18213-z.

K. C and J. M. Shivaram, “Segmentation of ovarian cyst using improved U-NET and hybrid deep learning model,†Multimed Tools Appl, vol. 83, no. 14, pp. 42645–42679, 2024, doi: 10.1007/s11042-023-16998-z.

S. Kurman and S. Kisan, “An in-depth and contrasting survey of meta-heuristic approaches with classical feature selection techniques specific to cervical cancer,†Knowl Inf Syst, vol. 65, no. 5, pp. 1881–1934, 2023, doi: 10.1007/s10115-022-01825-y.

V. S. Parvathy and S. Pothiraj, “Multi-modality medical image fusion using hybridization of binary crow search optimization,†Health Care Manag Sci, vol. 23, no. 4, pp. 661–669, 2020, doi: 10.1007/s10729-019-09492-2.

S. D. Rizki, Yuhandri, and I. Fitri, “Development of image extraction using the centerline method in the identification of appendicitis in ultrasonography,†Indonesian Journal of Electrical Engineering and Computer Science, vol. 36, no. 3, pp. 1750–1758, Dec. 2024, doi: 10.11591/ijeecs.v36.i3.pp1750-1758.

S. Rani and N. Kaur, “Medical X-ray images enhancement based on super resolution convolution neural network,†International Journal of Informatics and Communication Technology, vol. 13, no. 2, pp. 257–263, Aug. 2024, doi: 10.11591/ijict.v13i2.pp257-263.

I. U. W. Mulyono, E. H. Rachmawanto, C. A. Sari, and M. K. Sarker, “A high accuracy of deep learning based CNN architecture: classic, VGGNet, and RestNet50 for Covid-19 image classification,†Telkomnika (Telecommunication Computing Electronics and Control), vol. 22, no. 5, pp. 1187–1195, Oct. 2024, doi: 10.12928/TELKOMNIKA.v22i5.26017.

C. Anagha and K. Aishwarya, “PCOS detection using ultrasound images,†2021, Accessed: Dec. 20, 2024. [Online]. Available: https://www.kaggle.com/datasets/anaghachoudhari/pcos-detection-using-ultrasound-images.

D. Aldakheel, “Spine Trauma 10 years KFHU, Saudi Arabia datasheet Coded.xlsx,†Dec. 2023, doi: 10.6084/m9.figshare.22960979.v2.

Q. Zhao et al., “MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation,†Jul. 2022, [Online]. Available: http://arxiv.org/abs/2207.06799

Author Biographies

A. Boobalan

School of Computer Science and Engineering, Assistant Professor

P. Sudhakar

School of Computer Science and Engineering,

Professor & Program Chair

How to Cite

[1]
A. Boobalan and P. Sudhakar, “A Hybrid Deep Learning Framework for Accurate Polycystic Ovary Syndrome Detection Using Ultrasound Imaging”, IJEEI, vol. 13, no. 3, pp. 601–613, Sep. 2025, doi: 10.52549/ijeei.v13i3.6565.