PCOS detection from ovarian ultrasound images using feature fusion and hybrid classification approaches.

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Abstract

Polycystic Ovary Syndrome (PCOS) is a complex hormonal disorder that affects women of reproductive age globally. It is marked by hormonal imbalance, ovarian dysfunction, and metabolic problems, which may lead to serious health complications. Getting diagnosed early is crucial to detecting PCOS, with abdominal ultrasound being the most commonly used imaging modality. The conventional way of diagnosing PCOS relies on manual image assessment, which is time-consuming and can vary from person to person. To address existing limitations, this study proposes two automated PCOS classification frameworks based on deep feature fusion technique. Deep features were extracted from two complementary deep learning models, ResNet-18 and GoogLeNet, and fused to form a unified representation of ovarian characteristics. Then, the fused features were classified using two strategies: (i) an ensemble framework where AdaBoost was the meta-learner, and (ii) a lightweight custom classification head. The frameworks proposed in this study were evaluated against existing state-of-the-art approaches and demonstrated superior performance. The ensemble model achieved an accuracy of 97.36% with a training time of 16.44 s, while the custom classification head attained a higher accuracy of 99.19% with a reduced training time of 13.31 s. The proposed frameworks offer high accuracy and reduced training time for PCOS diagnosis. By integrating deep feature fusion with ensemble and lightweight classification approaches, this study provides an efficient and reliable solution that can support clinical decision-making and facilitate early PCOS diagnosis.

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