A retinal detachment based strabismus detection through FEDCNN.
Amblyopia
Convolutional neural network
Federated learning
Gaze deviation
Health issues
Strabismus
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
06 10 2024
06 10 2024
Historique:
received:
06
05
2024
accepted:
11
09
2024
medline:
7
10
2024
pubmed:
7
10
2024
entrez:
6
10
2024
Statut:
epublish
Résumé
Ocular strabismus, a common condition in the present generation is an absolute risk factor for amblyopia and blinding premorbid visual loss. Despite the availability of new optometry tools with eye-tracking data, the issues persist in attaining accuracy and reliability in diagnosing strabismus. These two concerns are specifically accommodated in this study by the proposed novel approach that involves CNNs with eye-tracking datasets from subjects. The presented work aims to improve the accuracy of diagnostics in ophthalmology utilizing the integration of the further proposed algorithms into an automatic strabismus detection system. For this purpose, the proposed FedCNN model combines the CNN with eXtreme Gradient Boosting (XGBoost) and uses the Gaze deviation (GaDe) images to capture dynamic eye movements. This method tries to make the feature extraction as accurate as possible in its best working state to enhance the diagnosis precision. The model proves to be accurate, reaching 95.2%, which is even more prominent because of the more or less detailed connection layer of the CNN, which is used for the selection of features designated for such tasks of strabismus recognition. The presented method has the potential of shifting the approach to diagnosing diseases of the eyes in more or less half of the patients.
Identifiants
pubmed: 39370435
doi: 10.1038/s41598-024-72919-6
pii: 10.1038/s41598-024-72919-6
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
23255Subventions
Organisme : Tahani Jaser Alahmadi
ID : Project number (PNURSP2024R513)
Informations de copyright
© 2024. The Author(s).
Références
Du, H.-Q. et al. Artificial intelligence-aided diagnosis and treatment in the field of optometry. Int. J. Ophthalmol. 16, 1406 (2023).
doi: 10.18240/ijo.2023.09.06
pubmed: 37724269
pmcid: 10475639
Nawaz, A., Ali, T., Mustafa, G., Babar, M. & Qureshi, B. Multi-class retinal diseases detection using deep cnn with minimal memory consumption. IEEE Access (2023).
Ziaei, S., Della Santina, L., Deiner, M., Grob, S. & Oatts, J. Accuracy of eyemeter as a deep learning tool for identifying strabismus in pediatric patients. J. Am. Assoc. Pediatr. Ophthalmol. Strabismus 26, e62 (2022).
doi: 10.1016/j.jaapos.2022.08.232
Muni, R. H. et al. Novel classification system for management of rhegmatogenous retinal detachment with minimally invasive detachment surgery: A network meta-analysis of randomized trials focused on patient-centred outcomes. Can. J. Ophthalmol. (2021).
Jabbar, M. K., Yan, J., Xu, H., Ur Rehman, Z. & Jabbar, A. Transfer learning-based model for diabetic retinopathy diagnosis using retinal images. Brain Sci. 12, 535 (2022).
doi: 10.3390/brainsci12050535
pubmed: 35624922
pmcid: 9139157
Jung, S.-M., Umirzakova, S. & Whangbo, T.-K. Strabismus classification using face features. In 2019 International Symposium on Multimedia and Communication Technology (ISMAC). 1–4 (IEEE, 2019).
Weinert, M. C. & Heidary, G. Pediatric ophthalmology and strabismus. In Pivotal Trials in Ophthalmology: A Guide for Trainees. 63–88 (2021).
Azar, A. T. A bio-inspired method for segmenting the optic disc and macula in retinal images. Int. J. Comput. Appl. Technol. 72, 262–277 (2023).
doi: 10.1504/IJCAT.2023.133882
Shi, D. & Tang, H. Research on strabismus iris segmentation model based on deep snake multitask learning. J. Electron. Imag. 31, 063018–063018 (2022).
doi: 10.1117/1.JEI.31.6.063018
Huang, X., Lee, S. J., Kim, C. Z. & Choi, S. H. An automatic screening method for strabismus detection based on image processing. PLoS One 16, e0255643 (2021).
doi: 10.1371/journal.pone.0255643
pubmed: 34343204
pmcid: 8330949
Zheng, C. et al. Detection of referable horizontal strabismus in children’s primary gaze photographs using deep learning. Transl. Vis. Sci. Technol. 10, 33–33 (2021).
doi: 10.1167/tvst.10.1.33
pubmed: 33532144
pmcid: 7846951
de Oliveira Simoes, T., Souza, J. C., de Almeida, J. D. S., Silva, A. C. & de Paiva, A. C. Automatic ocular alignment evaluation for strabismus detection using u-net and resnet networks. In 2019 8th Brazilian Conference on Intelligent Systems (BRACIS). 239–244 (IEEE, 2019).
Linde, G. et al. Automatic refractive error estimation using deep learning-based analysis of red reflex images. Diagnostics 13, 2810 (2023).
doi: 10.3390/diagnostics13172810
pubmed: 37685347
pmcid: 10486607
Pandey, N. N. & Muppalaneni, N. B. Strabismus free gaze detection system for driver’s using deep learning technique. Prog. Artif. Intell. 12, 45–59 (2023).
doi: 10.1007/s13748-023-00296-8
Santos, J. & Frango, I. Generating photorealistic images of people’s eyes with strabismus using deep convolutional generative adversarial networks. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). 1–4 (IEEE, 2020).
Yuan, Q. et al. Deep learning-based hybrid precoding for terahertz massive mimo communication with beam squint. IEEE Commun. Lett. 27, 175–179 (2022).
doi: 10.1109/LCOMM.2022.3211514
Hamid, H. S., AlKindy, B., Abbas, A. H. & Al-Kendi, W. B. An intelligent strabismus detection method based on convolution neural network. TELKOMNIKA (Telecommun. Comput. Electron. Control) 20, 1288–1296 (2022).
doi: 10.12928/telkomnika.v20i6.24232
de Figueiredo, L. A., Dias, J. V. P., Polati, M., Carricondo, P. C. & Debert, I. Strabismus and artificial intelligence app: Optimizing diagnostic and accuracy. Transl. Vis. Sci. Technol. 10, 22–22 (2021).
doi: 10.1167/tvst.10.7.22
pubmed: 34137838
pmcid: 8212438
Yang, Y. et al. Automatic identification of myopia based on ocular appearance images using deep learning. Ann. Transl. Med. 8 (2020).
Chen, Z., Fu, H., Lo, W.-L. & Chi, Z. Strabismus recognition using eye-tracking data and convolutional neural networks. J. Healthc. Eng. 2018 (2018).
Singh, L. K., Khanna, M. & Thawkar, S. A novel hybrid robust architecture for automatic screening of glaucoma using fundus photos, built on feature selection and machine learning-nature driven computing. Expert Syst. 39, e13069 (2022).
doi: 10.1111/exsy.13069
Jain, V. & Mangal, A. A novel 3d object watermarking technique using hash key cryptography. In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 1122–1126 (IEEE, 2021).
Akbulut, E., Kirik, F., Bayraktar, H., Mohammed, A. R. & Tuğcu, B. Determination of optimum hyperparameters in diagnosis of strabismus using artificial intelligence model: Cross-sectional study. Türk. Klin. J. Ophthalmol. 32 (2023).
Singh, L. K., Khanna, M., Thawkar, S. & Singh, R. Nature-inspired computing and machine learning based classification approach for glaucoma in retinal fundus images. Multimed. Tools Appl. 82, 42851–42899 (2023).
doi: 10.1007/s11042-023-15175-6
Singh, L. K., Pooja, Garg, H. & Khanna, M. An IOT based predictive modeling for glaucoma detection in optical coherence tomography images using hybrid genetic algorithm. Multimed. Tools Appl. 81, 37203–37242 (2022).
Singh, L. K. et al. Detection of glaucoma in retinal images based on multiobjective approach. Int. J. Appl. Evolut. Comput. (IJAEC) 11, 15–27 (2020).
doi: 10.4018/IJAEC.2020040102
Lu, J. et al. Automated strabismus detection for telemedicine applications. Preprint at arXiv:1809.02940 (2018).
Kamal, M. M. et al. A comprehensive review on the diabetic retinopathy, glaucoma and strabismus detection techniques based on machine learning and deep learning. Eur. J. Med. Health Sci. 4, 24–40 (2022).
Kaleem, S., Sohail, A., Tariq, M. U. & Asim, M. An improved big data analytics architecture using federated learning for IOT-enabled urban intelligent transportation systems. Sustainability 15, 15333 (2023).
doi: 10.3390/su152115333
Suriyal, S., Druzgalski, C. & Gautam, K. Quantitative assessment of strabismus and selected vision related anomalies. In World Congress on Medical Physics and Biomedical Engineering 2018: June 3–8, 2018, Prague, Czech Republic. Vol. 1. 103–108 (Springer, 2019).
Jabbar, A. et al. Deep transfer learning-based automated diabetic retinopathy detection using retinal fundus images in remote areas. Int. J. Comput. Intell. Syst. 17, 1–20 (2024).
Zhang, G. et al. Multi-feature fusion-based strabismus detection for children. IET Image Process. 17, 1590–1602 (2023).
doi: 10.1049/ipr2.12740
Chen, W. et al. Early detection of visual impairment in young children using a smartphone-based deep learning system. Nat. Med. 29, 493–503 (2023).
doi: 10.1038/s41591-022-02180-9
pubmed: 36702948
Kang, Y. C. et al. Automated mathematical algorithm for quantitative measurement of strabismus based on photographs of nine cardinal gaze positions. BioMed. Res. Int. 2022 (2022).
Mengash, H. A. & Hosni Mahmoud, H. A. Methodology for detecting strabismus through video analysis and intelligent mining techniques. Comput. Mater. Contin. 67 (2021).
Siddique, A. A. et al. Covid-19 classification from x-ray images: An approach to implement federated learning on decentralized dataset. Comput. Mater. Contin. 75, 3883–3901 (2023).
Zolkifli, N. S. & Nazari, A. Tracing of strabismus detection using Hough transform. In 2020 IEEE Student Conference on Research and Development (SCOReD), 313–318 (IEEE, 2020).
Angelov, P. & Gu, X. Mice: Multi-layer multi-model images classifier ensemble. In 2017 3rd IEEE International Conference on Cybernetics (CYBCONF), 1–8 (IEEE, 2017).
Mahmood, T., Rehman, A., Saba, T., Nadeem, L. & Bahaj, S. A. O. Recent advancements and future prospects in active deep learning for medical image segmentation and classification. IEEE Access (2023).
Peng, R. & Varshney, P. K. Noise-refined image enhancement using multi-objective optimisation. IET Image Process. 7, 191–200 (2013).
doi: 10.1049/iet-ipr.2011.0603
Mahmood, T., Saba, T., Rehman, A. & Alamri, F. S. Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography. Expert Syst. Appl. 123747 (2024).
Jabbar, A. et al. Brain tumor detection and multi-grade segmentation through hybrid caps-vggnet model. IEEE Access (2023).
Mohammadi, K., Islam, A. & Belhaouari, S. B. Zooming into clarity: Image denoising through innovative autoencoder architectures. IEEE Access (2024).
Jabbar, A. et al. A lesion-based diabetic retinopathy detection through hybrid deep learning model. IEEE Access (2024).
Tan, T. Y. et al. Evolving ensemble models for image segmentation using enhanced particle swarm optimization. IEEE Access 7, 34004–34019 (2019).
doi: 10.1109/ACCESS.2019.2903015
Madjarov, G., Gjorgjevikj, D. & Chorbev, I. A multi-class svm classifier utilizing binary decision tree. Informatica (2009).
Glasgow, M. R., Yuan, H. & Ma, T. Sharp bounds for federated averaging (local sgd) and continuous perspective. In International Conference on Artificial Intelligence and Statistics. 9050–9090 (PMLR, 2022).
Chatfield, K., Simonyan, K., Vedaldi, A. & Zisserman, A. Return of the devil in the details: Delving deep into convolutional nets. Preprint at arXiv:1405.3531 (2014).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition. 770–778 (2016).
Dosovitskiy, A. et al. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations (2020).
Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning. 6105–6114 (2019).
Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition 4700–4708 (2017).
Howard, A. et al. Searching for mobilenetv3. In Proc. of the IEEE/CVF International Conference on Computer Vision. 1314–1324 (2019).
Nair, V. & Hinton, G. E. Rectified linear units improve restricted Boltzmann machines. In Proc. of the 27th International Conference on Machine Learning (ICML-10). 807–814 (2010).
Zhang, X. et al. Attention to region: Region-based integration-and-recalibration networks for nuclear cataract classification using as-oct images. Med. Image Anal. 80, 102499 (2022).
doi: 10.1016/j.media.2022.102499
pubmed: 35704990
Zhang, X. et al. Regional context-based recalibration network for cataract recognition in as-oct. Pattern Recognit. 147, 110069 (2024).
doi: 10.1016/j.patcog.2023.110069
Miao, Y., Jeon, J. Y., Park, G., Park, S. W. & Heo, H. Virtual reality-based measurement of ocular deviation in strabismus. Comput. Methods Prog. Biomed. 185, 105132 (2020).
doi: 10.1016/j.cmpb.2019.105132
Mao, K. et al. An artificial intelligence platform for the diagnosis and surgical planning of strabismus using corneal light-reflection photos. Ann. Transl. Med. 9 (2021).
Zhang, X. et al. Pyramid pixel context adaption network for medical image classification with supervised contrastive learning. IEEE Transactions on Neural Networks and Learning Systems (2024).
Li, J. et al. Evaluation of streamed hardware-to-software telemedicine strabismus consultations utilizing video glasses. Clin. Ophthalmol. (Auckland, NZ) 16, 3927 (2022).
Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).