PCOSFusion：一种基于HOG-LBP特征的混合方法，使用StackPCOS和StackBoostPCOS进行PCOS分类
PCOSFusion: a hybrid HOG-LBP feature-based approach for PCOS classification using StackPCOS and StackBoostPCOS.
摘要(中文译文)
多囊卵巢综合征（PCOS）是一种影响女性生殖健康的常见疾病，通过图像分析进行早期、准确的检测可显著辅助诊断。本研究提出了一种用于PCOS自动二分类的混合方法，该方法将先进的特征提取技术与堆叠集成学习模型相结合。研究考察了两种策略。第一种方法采用四个分类器的堆叠集成，第二种方法引入梯度提升（GB）作为额外的基学习器，使集成规模增至五个分类器。在特征提取阶段采用PCOSFusion算法以识别卵巢医学图像中的独特模式。提取的特征随后输入分类器进行训练和评估。两种策略均能有效区分PCOS（异常）与非PCOS（正常）病例。结果表明，堆叠集成方法发挥了各分类器的互补优势，其中引入梯度提升的第二种方法取得了轻微的性能提升。表现最佳的模型达到了98.44%的准确率、99.35%的精确率和98.49%的召回率，凸显了基于堆叠的集成技术与有效特征提取相结合在提升医学影像任务诊断准确性方面的潜力。这些发现支持所提方法作为辅助医疗专业人员早期检测PCOS的有价值工具的可行性。
Abstract
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.
如何引用
AMA
Palvi Soni, Chirag Sharma, Deepak Prashar, Arfat Ahmad Khan, Jungeun Kim, Seifedine Kadry. PCOSFusion: a hybrid HOG-LBP feature-based approach for PCOS classification using StackPCOS and StackBoostPCOS.. Scientific reports. 2026; doi:10.1038/s41598-026-60667-8.
APA
Palvi Soni, Chirag Sharma, Deepak Prashar, Arfat Ahmad Khan, Jungeun Kim, Seifedine Kadry (2026). PCOSFusion: a hybrid HOG-LBP feature-based approach for PCOS classification using StackPCOS and StackBoostPCOS.. Scientific reports. https://doi.org/10.1038/s41598-026-60667-8
GB/T 7714
Palvi Soni, Chirag Sharma, Deepak Prashar, Arfat Ahmad Khan, Jungeun Kim, Seifedine Kadry. PCOSFusion: a hybrid HOG-LBP feature-based approach for PCOS classification using StackPCOS and StackBoostPCOS.[J]. Scientific reports, 2026 doi:10.1038/s41598-026-60667-8.
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