An improved approach for early diagnosis of Parkinson’s disease using advanced DL models and image alignment

References

• Vyas T, Yadav R, Solanki C, et al. Deep learning-based scheme to diagnose Parkinson's disease. Expert Syst. 2022;39(3):e12739. doi:10.1111/exsy.12739
   Web of Science ®Google Scholar
• Noor MBT, Zenia NZ, Kaiser MS, et al. Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia. Brain Inform. 2020;7:1–21.
   PubMedGoogle Scholar
• Gupta R, Kumari S, Senapati A, et al. New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson’s disease. Ageing Res Rev. 2023;102013.
   Google Scholar
• Aggarwal N, Saini BS, Gupta S. Role of artificial intelligence techniques and neuroimaging modalities in detection of Parkinson’s disease: a systematic review. Cognit Comput. 2023: 1–38.
   Web of Science ®Google Scholar
• Zhang J. Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson’s disease. NPJ Parkinson's Dis. 2022;8(1):13. doi:10.1038/s41531-021-00266-8
   PubMed Web of Science ®Google Scholar
• Marino S, Ciurleo R, Di Lorenzo G, et al. Magnetic resonance imaging markers for early diagnosis of Parkinson's disease. Neural Regen Res. 2012;7(8):611.
   PubMed Web of Science ®Google Scholar
• Robitaille MC, Byers JM, Christodoulides JA, et al. A self-supervised machine learning approach for objective live cell segmentation and analysis. bioRxiv. 2021.
   Google Scholar
• Khalifa NM, Taha MH, Ezzat Ali D, et al. Artificial intelligence technique for gene expression by tumor RNA-seq data: a novel optimized deep learning approach. IEEE Access. 2020;8:22874–22883. doi:10.1109/ACCESS.2020.2970210
   Google Scholar
• Cardone D, Perpetuini D, Filippini C, et al. Driver stress state evaluation by means of thermal imaging: a supervised machine learning approach based on ECG signal. Appl Sci. 2020;10(16):5673.
   Google Scholar
• Chang C, Hung J, Hu Y, et al. Prediction of preoperative blood preparation for orthopedic surgery patients: a supervised learning approach. Appl Sci. 2018.
   Google Scholar
• Zhang B, Wang Y, Hou W, et al. Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling. Adv Neural Inf Process. 2021;34:18408–18419.
   Google Scholar
• Han D, Jung J, Kwon S. Comparative study on supervised learning models for productivity forecasting of shale reservoirs based on a data-driven approach. Appl Sci. 2020.
   Google Scholar
• Afham M, Dissanayake I, Dissanayake D, et al. CrossPoint: self-supervised cross-modal contrastive learning for 3D point cloud understanding. 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR); 2022. p. 9892–9902.
   Google Scholar
• Huang L, You S, Zheng M, et al. Learning where to learn in cross-view self-supervised learning. 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2022. p. 14431–14440.
   Google Scholar
• Sajun AR, Zualkernan IA. Survey on implementations of generative adversarial networks for semi-supervised learning. Appl Sci. 2022;12(3):1718.
   Google Scholar
• Chen Z, Ge J, Zhan H, et al. Pareto self-supervised training for few-shot learning. 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR); 2021. p. 13658–13667.
   Google Scholar
• Arab I, Fondrie WE, Laukens K, et al. Semi-supervised machine learning for sensitive open modification spectral library searching. bioRxiv. 2022.
   Google Scholar
• Pashaian M, Seyedin S, Ahadi SM. A novel jointly optimized cooperative DAE-DNN approach based on a new multi-target step-wise learning for speech enhancement. IEEE Access. 2023;11:21669–21685. doi:10.1109/ACCESS.2023.3250820
   Web of Science ®Google Scholar
• Nienhaus J, Matten P, Britten A, et al. Live 4D-OCT denoising with self-supervised deep learning. Sci Rep. 2023;13(1):5760.
   PubMed Web of Science ®Google Scholar
• Huang B, Zhang E, Chaudhari R, et al. Sequence-based optimized chaos game representation and deep learning for peptide/protein classification. bioRxiv. 2022.
   Google Scholar
• Yang K, Sun Y, Su J, et al. Adversarial auto-augment with label preservation: a representation learning principle guided approach. ArXiv, abs/2211.00824. 2022.
   Google Scholar
• Kolarík M, Burget R, Uher V, et al. Optimized high resolution 3D dense-U-Net network for brain and spine segmentation. Appl Sci. 2019.
   Google Scholar
• Qadri SF, Ai D, Hu G, et al. Automatic deep feature learning via patch-based deep belief network for vertebrae segmentation in CT images. Appl Sci. 2018.
   Google Scholar
• Li S, Gong K, Liu CH, et al. MetaSAug: meta semantic augmentation for long-tailed visual recognition. 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR); 2021. p. 5208–5217.
   Google Scholar
• Grill J, Strub F, Altch'e F, et al. Bootstrap your own latent: a new approach to self-supervised learning. ArXiv, abs/2006.07733. 2020.
   Google Scholar
• Fu Y, Wang X. Category-level 6D object pose estimation in the wild: a semi-supervised learning approach and a new dataset. ArXiv, abs/2206.15436. 2022.
   Google Scholar
• Berthelot D, Carlini N, Goodfellow IJ, et al. MixMatch: a holistic approach to semi-supervised learning. ArXiv, abs/1905.02249. 2019.
   Google Scholar
• Qayyum A, Tahir A, Butt MA, et al. Dental caries detection using a semi-supervised learning approach. Sci Rep. 2023;13.
   Google Scholar
• Sadeghi M, Akbari H, Mousavi S, et al. Coherency-based supervised learning approach for determining optimal post-disturbance system separation strategies. IEEE Access. 2023;11:5894–5907. doi:10.1109/ACCESS.2022.3227512
   Web of Science ®Google Scholar
• Hong G, Kim Y. Supervised learning approach for state estimation of unmeasured points of distribution network. IEEE Access. 2020;8:113918–113931. doi:10.1109/ACCESS.2020.3003049
   Web of Science ®Google Scholar
• Indira DNVSLS, Ganiya RK, Ashok Babu P, et al. Improved artificial neural network with state order dataset estimation for brain cancer cell diagnosis. BioMed Res Int. 2022;2022.
   PubMed Web of Science ®Google Scholar
• Zhai X, Rajaram A, Ramesh K. Cognitive model for human behavior analysis. J Interconnect Netw. 2022;22(Supp04):2146013. doi:10.1142/S0219265921460130
   Web of Science ®Google Scholar
• Poloju N, Rajaram A. Data mining techniques for patients healthcare analysis during covid-19 pandemic conditions. J Environ Prot Ecol. 2022;23(5):2105–2112.
   Web of Science ®Google Scholar
• Kalaivani K, Kshirsagarr PR, Sirisha Devi J, et al. Prediction of biomedical signals using deep learning techniques. J Intell Fuzzy Syst. 2023;Preprint:1–14.
   Google Scholar
• Martinez-Eguiluz M, Arbelaitz O, Gurrutxaga I, et al. Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies. Neural Comput Appl. 2023;35(8):5603–5617. doi:10.1007/s00521-022-07256-8
   Web of Science ®Google Scholar