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FLinkNet-GAN: An integrated deep learning and fuzzy logic architecture for membership based cerebral palsy risk assessment | ||
| Iranian Journal of Fuzzy Systems | ||
| دوره 23، شماره 6، بهمن و اسفند 2026، صفحه 139-156 اصل مقاله (2 M) | ||
| نوع مقاله: Original Manuscript | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2026.54858.9720 | ||
| نویسندگان | ||
| M. Sweetline Sonia* ؛ S. Sumathi | ||
| Mahendra Engineering College | ||
| چکیده | ||
| Early detection of cerebral palsy (CP) is challenging due to subtle and heterogeneous neuromotor abnormalities in infants. This paper proposes FLinkNet-GAN, a hybrid deep learning and fuzzy logic framework for membership-based CP risk assessment from RGB-D infant videos. Initially, a Bright Contrast Dynamic Histogram Equalization (BCDHE) filter enhances motion visibility. A Generative Adversarial Network (GAN) performs multi-pose estimation to extract kinematic features, including Movement Deviation (MD) and Joint Trajectory Discontinuity (JD). These features are processed through a LinkNet backbone to preserve fine spatial details and subsequently mapped into a Mamdani-type Fuzzy Inference System using Gaussian membership functions. Unlike conventional hard classifiers, the proposed approach generates a continuous Risk Score (RS) via centroid defuzzification, providing interpretable risk levels: Normal, Borderline, High, and Very High. Experimental evaluation on the RVI-38 dataset achieves 94.85% accuracy and 94.5% pose precision, demonstrating robust and uncertainty-aware CP risk stratification. | ||
| کلیدواژه‌ها | ||
| Cerebral palsy detection؛ fuzzy inference system؛ generative adversarial network؛ pose estimation؛ Gaussian membership function؛ risk score modeling | ||
| مراجع | ||
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