| تعداد نشریات | 31 |
| تعداد شمارهها | 864 |
| تعداد مقالات | 8,335 |
| تعداد مشاهده مقاله | 16,775,871 |
| تعداد دریافت فایل اصل مقاله | 11,208,282 |
FLinkNet-GAN: An integrated deep learning and fuzzy logic architecture for membership based cerebral palsy risk assessment | ||
| Iranian Journal of Fuzzy Systems | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 30 شهریور 1405 اصل مقاله (2 M) | ||
| نوع مقاله: Original Manuscript | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2026.54858.9720 | ||
| نویسندگان | ||
| Sweetline Sonia M.* ؛ Sumathi S | ||
| 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 | ||
|
[1] A. M. Al-Sowi, N. AlMasri, B. Hammo, F. A. Z. A. Al-Qwaqzeh, Cerebral palsy classification based on multi feature analysis using machine learning, Informatics in Medicine Unlocked, 37 (2023), 101197. https://doi.org/ 10.1016/j.imu.2023.101197
[2] Y. Choi, M. Choi, M. Kim, et al., StarGAN: Unified generative adversarial networks for multi-domain image-to image translation, In IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 8789-8797. https://doi.org/10.1109/CVPR.2018.00916
[3] P. Coupeau, J. B. Fasquel, J. Dèmas, et al., Detecting cerebral palsy in neonatal stroke children: GNN-based detection considering the structural organization of basal ganglia, In IEEE 20th International Symposium on Biomedical Imaging (ISBI), (2023), 1-6. https://doi.org/10.1109/ISBI53787.2023.10230380
[4] D. S. Dakshina, P. Jayapriya, R. Kala, Saree texture analysis and classification via deep learning framework, International Journal of Data Science and Artificial Intelligence, 1(1) (2023), 20-25.
[5] R. K. Devarajan, S. S. Khader, Pose sequence-aware generative adversarial network for augmenting skeleton sequences to improve cerebral palsy detection, International Journal of Intelligent Engineering and Systems, 16(5) (2023), 1-12. https://doi.org/10.22266/ijies2023.1031.44
[6] R. Dinesh Jackson Samuel, E. Fenil, G. Manogaran, et al., Real time violence detection framework using bidirectional LSTM, Computer Networks, 151 (2019), 191-200. https://doi.org/10.1016/j.comnet.2019.01.028
[7] K. Gayathri, K. P. Ajitha Gladis, A. Angel Mary, Real time masked face recognition using deep learning based YOLOv4 network, International Journal of Data Science and Artificial Intelligence, 1(1) (2023), 26-32. https: //doi.org/10.66135/ijdsai0101p004
[8] Y. Gu, R. E. Wijesinghe, D. Seong, et al., Development of Pilates-based exercise program for dynamic balancing of adult cerebral palsy, Healthcare, 12(2) (2024), 251. https://doi.org/10.21203/rs.3.rs-3790528/v1
[9] M. Hadders-Algra, Early diagnostics and early intervention in neurodevelopmental disorders, Journal of Clinical Medicine, 10(4) (2021), 861. https://doi.org/10.3390/jcm10040861
[10] M. A. Hedjazi, Y. Genc, Efficient texture-aware multi-GAN for image inpainting, Knowledge-Based Systems, 217 (2021), 106789. https://doi.org/10.1016/j.knosys.2021.106789
[11] V. Horber, U. Grasshoff, E. Sellier, et al., The role of neuroimaging and genetic analysis in cerebral palsy diagnosis, Frontiers in Neurology, 11 (2021), 628075. https://doi.org/10.3389/fneur.2020.628075
[12] K. Iqbal, X. Yu, A. Rafique, et al., CGE-GAN: Contrastive-guided evolutionary generative adversarial networks with dynamic adaptive weight sharing, Neural Networks, 199 (2026), 108702. https://doi.org/10.1016/j.neunet. 2026.108702
[13] D. I. J. Jacob, Performance evaluation of caps-net based multitask learning architecture, Journal of Artificial Intelligence and Capsule Networks, 2(1) (2020), 1-10. https://doi.org/10.36548/jaicn.2020.1.001
[14] T. R. Khalifa, A. M. El-Nagar, M. A. El-Brawany, et al., A novel fuzzy Wiener-based nonlinear modelling for engineering applications, ISA Transactions, 97 (2020), 130-142. https://doi.org/10.1016/j.isatra.2019.07. 017
[15] T. R. Khalifa, A. M. El-Nagar, M. A. El-Brawany, et al., A novel Hammerstein model for nonlinear networked systems based on an interval type-2 fuzzy Takagi-Sugeno-Kang system, IEEE Transactions on Fuzzy Systems, 29(2) (2020), 275-285. https://doi.org/10.1109/TFUZZ.2020.3007460
[16] T. R. Khalifa, X. Yu, A. Sharafian, et al., Interval type-3 fuzzy Wiener model for nonlinear dynamic systems: Application to continuous stirred tank reactor, Chaos, Solitons and Fractals, 199(Part 1) (2025), 116584. https: //doi.org/10.1016/j.chaos.2025.116584
[17] Y. M. Kim, S. Ashwal, Diagnosis of cerebral palsy, Neurodevelopmental Pediatrics, (2023), 497-513. https://doi. org/10.1007/978-3-031-20792-1_30
[18] C. Lidbeck, Å. Bartonek, A. Ferrari, et al., Signs of perceptual disorder during movement were reliably assessed in children with cerebral palsy in Sweden, Acta Paediatrica, 113(2) (2024), 344-352. https://doi.org/10.1111/ apa.17012
[19] K. D. McCay, et al., A pose-based feature fusion and classification framework for the early prediction of cerebral palsy in infants, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30 (2021), 8-19. https: //doi.org/10.1109/TNSRE.2021.3138185
[20] P. Mendoza-Sengco, C. L. Chicoine, J. Vargus-Adams, Early cerebral palsy detection and intervention, Pediatric Clinics of North America, 70(3) (2023), 385-398. https://doi.org/10.1016/j.pcl.2023.01.014
[21] C. Morgan, N. Badawi, I. Novak, A qualitative study of parents’ experience with early diagnosis and GAME intervention, Journal of Clinical Medicine, 12(2) (2023), 583. https://doi.org/10.3390/jcm12020583
[22] B. E. Ostrander, N. L. Maitre, A. F. Duncan, Early detection of cerebral palsy, Principles of Neonatology, (2024), 802-811. https://doi.org/10.1016/B978-0-323-69415-5.00094-1
[23] P. Palraj, G. Siddan, Deep learning algorithm for classification of cerebral palsy from fMRI, International Journal of Advanced Computer Science and Applications, 12(3) (2021), 1-8. https://doi.org/10.14569/IJACSA.2021. 0120387
[24] A. Rafique, Y. Xue, M. Alhussein, et al., Differential evolutionary architecture search with dynamic similarity-aware weight sharing for optimization of GANs, Neurocomputing, 671 (2026), 132619. https://doi.org/10.1016/j. neucom.2026.132619
[25] A. Rafique, X. Yu, K. Iqbal, et al., MD-EGAN: Evolutionary GAN with dynamic latent sampling and relative adaptive discriminator for improved performance, Neurocomputing, 664 (2025), 131951. https://doi.org/10. 1016/j.neucom.2025.131951
[26] D. Sakkos, et al., Identification of abnormal movements in infants using deep neural networks, IEEE Access, 9 (2021), 94281-94292.
[27] C. A. Sarmiento, et al., “We do it all”: Caregiver role for young adults with cerebral palsy, Health Care Transitions, 2 (2024), 100039. https://doi.org/10.1016/j.hctj.2023.100039
[28] R. Shalaby, M. Ibrahim, T. Khalifa, Fuzzy-supervised PI for flow-control process, design and experimental validation, Proceedings of the 2019 Novel Intelligent and Leading Emerging Sciences Conference (NILES), (2019), 249-253. https://doi.org/10.1109/NILES.2019.8909327
[29] R. Shalaby, T. Khalifa, M. Ibrahim, A novel scheme for the identification of nonlinear flow control process based on fuzzy tuning parameters, Proceedings of the 11th International Computer Engineering Conference (ICENCO), (2015), 52-57. https://doi.org/10.1109/ICENCO.2015.7416325
[30] B. Sivasankari, et al., High-throughput and power-efficient convolutional neural network, Journal of Circuits, Systems and Computers, 31(13) (2022), 2250226. https://doi.org/10.1142/S0218126622502267
[31] Z. Wang, Y. Xue, F. Neri, Multi-population co-evolutionary generative adversarial network architecture search for zero-shot learning, IEEE Transactions on Evolutionary Computation, (2026), 1-15. https://doi.org/10.1109/ TEVC.2026.3650926
[32] Y. Xu, Y. Li, S. A. Richard, et al., Genetic pathways in cerebral palsy, Neural Regeneration Research, 19(7) (2024), 1499-1508. https://doi.org/10.4103/1673-5374.385855
[33] D. Yoon, et al., DIFAI: Diverse facial inpainting using StyleGAN inversion, IEEE International Conference on Image Processing (ICIP), (2022), 1-6. https://doi.org/10.1109/ICIP46576.2022.9898012
[34] H. Zhang, E. S. Ho, H. P. Shum, CP-AGCN: Attention informed graph convolutional network for identifying infants at risk of cerebral palsy, Software Impacts, 14 (2022), 100419. https://doi.org/10.1016/j.simpa.2022.100419
[35] H. Zhang, H. P. H. Shum, E. S. L. Ho, Cerebral palsy prediction with frequency attention informed GCN, 2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2022), 1-6. https://doi.org/10.1109/EMBC48229.2022.9871230
[36] M. Zhu, Q. Men, E. S. Ho, et al., Interpreting deep learning based cerebral palsy prediction with channel attention, 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), (2021), 1-6. https: //doi.org/10.1109/BHI50953.2021.9508619 | ||
|
آمار تعداد مشاهده مقاله: 4 تعداد دریافت فایل اصل مقاله: 2 |
||