PCOSFusion: a hybrid HOG-LBP feature-based approach for PCOS classification using StackPCOS and StackBoostPCOS.

Polycystic Ovary Syndrome (PCOS) is a prevalent condition affecting female reproductive health, where early and accurate detection through image analysis can significantly aid diagnosis. This study proposes a hybrid approach for automated binary classification of PCOS that integrates advanced feature extraction techniques with stacking ensemble learning models. Two strategies are investigated. The first approach employs a stacking ensemble of four classifiers, while the second approach introduces Gradient Boosting (GB) as an additional base learner, increasing the ensemble to five classifiers. The PCOSFusion algorithm is utilized during feature extraction to identify distinctive patterns in ovarian medical images. Extracted features are then input to the classifiers for training and evaluation. Both strategies effectively distinguish between PCOS (abnormal) and non-PCOS (normal) cases. Results demonstrate that the stacking ensemble method harnesses the complementary strengths of individual classifiers, with the second approach, which incorporates Gradient Boosting, achieving a slight performance improvement. The best-performing model achieved 98.44% accuracy, 99.35% precision, and 98.49% recall, highlighting the potential of stacking-based ensemble techniques combined with effective feature extraction to improve diagnostic accuracy in medical imaging tasks. These findings support the viability of the proposed method as a valuable tool for assisting medical professionals in the early detection of PCOS.