Publisher

IAESONLINEIAESONLINE

Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)Indonesian Journal of Electrical Engineering and Informatics (IJEEI) 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.

Conclusion

This study presents a hybrid deep learning framework, CWANN–NBFA MMHAO, for accurate PCOS diagnosis from ultrasound images.The framework achieves superior performance across three benchmark datasets, with accuracies ranging from 98.Statistical analysis confirms the significance of these improvements.The proposed method demonstrates robustness and effective handling of uncertain data, outperforming classical image processing, CNN-based, and hybrid machine learning approaches.

Future Research

Further research should focus on expanding the dataset to include more diverse patient samples to improve the robustness of the model. Investigating the integration of multi-modal data, such as clinical data alongside ultrasound images, could provide a more comprehensive diagnostic approach. Additionally, exploring methods to enhance model interpretability is crucial for building trust and facilitating clinical adoption. These advancements will contribute to the development of a more reliable and clinically applicable PCOS detection system, ultimately improving patient care and outcomes by enabling earlier and more accurate diagnoses. The proposed framework can be extended to incorporate transformer- or transfer learning-based models for further performance enhancement, and large-scale multi-centre clinical validation is needed to assess its real-world applicability.

References

1. Transfer learning scenarios on deep learning for ultrasoundbased image segmentation | Bani Unggul | IAES... doi.org/10.11591/ijai.v13.i3.pp3273-3282Transfer learning scenarios on deep learning for ultrasoundbased image segmentation Bani Unggul IAES doi 10 11591 ijai v13 i3 pp3273 3282
2. Medical X-ray images enhancement based on super resolution convolution neural network | Rani | International... doi.org/10.11591/ijict.v13i2.pp257-263Medical X ray images enhancement based on super resolution convolution neural network Rani International doi 10 11591 ijict v13i2 pp257 263
3. A model for classifying breast masses in ultrasound images | Morsy | International Journal of Advances... doi.org/10.11591/ijaas.v13.i3.pp566-578A model for classifying breast masses in ultrasound images Morsy International Journal of Advances doi 10 11591 ijaas v13 i3 pp566 578

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