Deep Learning Applications for Dyslexia Prediction
Source abstract
Abstract
Dyslexia is a neurological problem that leads to obstacles and difficulties in the learning process, especially in reading. Generally, people with dyslexia suffer from weak reading, writing, spelling, and fluency abilities. However, these difficulties are not related to their intelligence. An early diagnosis of this disorder will help dyslexic children improve their abilities using appropriate tools and specialized software. Machine learning and deep learning methods have been implemented to recognize dyslexia with various datasets related to dyslexia acquired from medical and educational organizations. This review paper analyzed the prediction performance of deep learning models for dyslexia and summarizes the challenges researchers face when they use deep learning models for classification and diagnosis. Using the PRISMA protocol, 19 articles were reviewed and analyzed, with a focus on data acquisition, preprocessing, feature extraction, and the prediction model performance. The purpose of this review was to aid researchers in building a predictive model for dyslexia based on available dyslexia-related datasets. The paper demonstrated some challenges that researchers encounter in this field and must overcome.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: EEG and Brain-Computer Interfaces · Reading and Literacy Development · Tactile and Sensory Interactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Information Systems, King Khaled University, Abha 61421, Saudi Arabia
References (49)
Show source-provided references
1. Learning disabilities, dyslexia, and vision: The role of the pediatric ophthalmologist10.1016/j.jaapos.2011.03.003 ↗
2. Dyslexia : An overview
3. Laffey, J.L., and Siffin, C.F. (2023, January 26). ERIC/CRIER Reading Review Series, Available online: https://files.eric.ed.gov/fulltext/ED032453.pdf.
4. CNN Comparisons Models on Dyslexia Handwriting Classification
5. ICTs as a distinct detection approach for dyslexia screening: A contemporary view10.3991/ijoe.v15i13.11011 ↗
6. Biomarker-based approaches for dyslexia screening: A review
7. The neurological basis of developmental dyslexia an overview and working hypothesis10.1093/brain/123.12.2373 ↗
8. Deviant processing of letters and speech sounds as proximate cause of reading failure: A functional magnetic resonance imaging study of dyslexic children10.1093/brain/awp308 ↗
9. White matter pathways mediate parental effects on children’s reading precursors10.1016/j.bandl.2017.05.002 ↗
10. Current perspectives on the cerebellum and reading development10.1016/j.neubiorev.2018.05.006 ↗
11. Different brain activation patterns in dyslexic children: Evidence from EEG power and coherence patterns for the double-deficit theory of dyslexia10.1142/S0219635207001404 ↗
12. Usman, O.L., and Muniyandi, R.C. (2020). CryptoDL: Predicting dyslexia biomarkers from encrypted neuroimaging dataset using energy-efficient residue number system and deep convolutional neural network. Symmetry, 12.10.3390/sym12050836 ↗
13. Sharma, D.K., Chatterjee, M., Kaur, G., and Vavilala, S. (2022). Deep Learning Applications for Disease Diagnosis, Elsevier.10.1016/B978-0-12-824145-5.00005-8 ↗
14. On deep learning for medical image analysis10.1001/jama.2018.13316 ↗
15. Prediction of Dyslexia Using Machine Learning—A Research Travelogue
16. Developmental dyslexia detection using machine learning techniques: A survey10.1016/j.icte.2020.05.006 ↗
17. Dyslexia Prediction Using Machine Learning Algorithms—A Review
18. Dhas, J., and Jose, S.H. (2023, January 26). A Study on Dyslexia Using Machine Learning-Review. EasyChair Prepr.. Available online: https://easychair.org/publications/preprint/gLjk.
19. Advance Machine Learning Methods for Dyslexia Biomarker Detection: A Review of Implementation Details and Challenges10.1109/ACCESS.2021.3062709 ↗
20. Kothapalli, P.K.V., Rathikarani, V., and Nookala, G.K.M. (2022). Inventive Systems and Control, Springer.
21. Ahire, N., Awale, R., Patnaik, S., and Wagh, A. (2022). A comprehensive review of machine learning approaches for dyslexia diagnosis. Multimed. Tools Appl., 1–12.10.1007/s11042-022-13939-0 ↗
22. Application of Machine Learning Techniques for Improving Learning Disabilities
23. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement10.1016/j.jclinepi.2009.06.005 ↗
24. Why children with dyslexia struggle with writing and how to help them10.1044/2018_LSHSS-DYSLC-18-0024 ↗
25. Spoon, K., Crandall, D., and Siek, K. (2019, January 9–15). Towards Detecting Dyslexia in Children’s Handwriting Using Neural Networks. Proceedings of the International Conference on Machine Learning AI for Social Good Workshop, Long Beach, CA, USA.
26. Spoon, K., Siek, K., Crandall, D., and Fillmore, M. (2019, January 9–15). Can We (and Should We) Use AI to Detect Dyslexia in Children’s Handwriting?. Proceedings of the International Conference on Machine Learning AI for Social Good Workshop, Long Beach, CA, USA.
27. Automated Detection of Dyslexia Symptom Based on Handwriting Image for Primary School Children10.1016/j.procs.2019.12.127 ↗
28. Yogarajah, P. (2021, January 10–15). Deep Learning Approach to Automated Detection of Dyslexia-Dysgraphia. Proceedings of the 25th IEEE International Conference on Pattern Recognition, Milan, Italy. Available online: https://www.micc.unifi.it/icpr2020/.
29. Bin Rosli, M.S.A., Isa, I.S., Ramlan, S.A., Sulaiman, S.N., and Maruzuki, M.I.F. (2021, January 27–28). Development of CNN Transfer Learning for Dyslexia Handwriting Recognition. Proceedings of the 2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia.10.1109/ICCSCE52189.2021.9530971 ↗
30. Kohli, M., and Prasad, T.V. (July, January 30). Identifying dyslexic students by using artificial neural networks. Proceedings of the World Congress on Engineering 2010, London, UK.
31. Review of EEG-based pattern classifcation frameworks for dyslexia10.1186/s40708-018-0079-9 ↗
32. Karim, I., Abdul, W., and Kamaruddin, N. (2013, January 26–27). Classification of Dyslexic and Normal Children during resting condition using KDE and MLP. Proceedings of the 2013 5th International Conference on Information and Communication Technology for the Muslim World, Rabat, Morocco.10.1109/ICT4M.2013.6518886 ↗
33. Al-Barhamtoshy, H.M., and Motaweh, D.E.M. (2017, January 21–23). Diagnosis of Dyslexia using Computing Analysis. Proceedings of the 2017 International Conference on Informatics, Health & Technology (ICIHT), Riyadh, Saudi Arabia.10.1109/ICIHT.2017.7899141 ↗
34. Frid, A., and Manevitz, L.M. (2018). Features and Machine Learning for Correlating and Classifying between Brain Areas and Dyslexia. arXiv.
35. Ortiz, A., Martínez-Murcia, F.J., Formoso, M.A., Luque, J.L., and Sánchez, A. (2020). Hybrid Artificial Intelligent Systems, Springer.
36. Magnetic resonance imaging findings for dyslexia: A review10.1166/jbn.2014.1895 ↗
37. Dyslexia detection using 3D convolutional neural networks and functional magnetic resonance imaging10.1016/j.cmpb.2020.105726 ↗
38. Automatic classification of dyslexic children by applying machine learning to fMRI images10.3233/BME-141120 ↗
39. Visual Explanation for Identification of the Brain Bases for Developmental Dyslexia on fMRI Data10.3389/fncom.2021.594659 ↗
40. Eye tracking based dyslexia detection using a holistic approach10.1038/s41598-021-95275-1 ↗
41. Eye Movement Feature Set and Predictive Model for Dyslexia: Feature Set and Predictive Model for Dyslexia10.4018/IJCINI.20211001.oa28 ↗
42. Benfatto, M.N., Seimyr, G., Ygge, J., Pansell, T., Rydberg, A., and Jacobson, C. (2016). Screening for dyslexia using eye tracking during reading. PLoS ONE, 11.10.1371/journal.pone.0165508 ↗
43. Vajs, I., Kovic, V., Papic, T., Savic, A.M., and Jankovic, M.M. (September, January 29). Dyslexia detection in children using eye tracking data based on VGG16 network. Proceedings of the European Signal Processing Conference, Belgrade, Serbia.
44. A novel approach for detection of dyslexia using convolutional neural network with EOG signals10.1007/s11517-022-02656-3 ↗
45. Xing, L., and Qiao, Y. (2016, January 23–26). DeepWriter: A multi-stream deep CNN for text-independent writer identification. Proceedings of the International Conference on Frontiers in Handwriting Recognition, ICFHR, Shenzhen, China.10.1109/ICFHR.2016.0112 ↗
46. Dara, S., and Tumma, P. (2018, January 29–31). Feature Extraction By Using Deep Learning: A survey. Proceedings of the 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India.10.1109/ICECA.2018.8474912 ↗
47. Khan, A., Sohail, A., Zahoora, U., and Qureshi, A.S. (2020). A Survey of the Recent Architectures of Deep Convolutional Neural Networks, Springer.10.1007/s10462-020-09825-6 ↗
48. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions10.1186/s40537-021-00444-8 ↗
49. A survey on Image Data Augmentation for Deep Learning10.1186/s40537-019-0197-0 ↗