Deep Learning Applications for Dyslexia Prediction
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/5/2804 |
| _version_ | 1797615784889942016 |
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| author | Norah Dhafer Alqahtani Bander Alzahrani Muhammad Sher Ramzan |
| author_facet | Norah Dhafer Alqahtani Bander Alzahrani Muhammad Sher Ramzan |
| author_sort | Norah Dhafer Alqahtani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T07:31:45Z |
| format | Article |
| id | doaj.art-515b22c8111c453fba98913f6326bb2a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T07:31:45Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-515b22c8111c453fba98913f6326bb2a2023-11-17T07:15:18ZengMDPI AGApplied Sciences2076-34172023-02-01135280410.3390/app13052804Deep Learning Applications for Dyslexia PredictionNorah Dhafer Alqahtani0Bander Alzahrani1Muhammad Sher Ramzan2Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaFaculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaFaculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDyslexia 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.https://www.mdpi.com/2076-3417/13/5/2804dyslexia detectiondyslexia classificationfeature extractiondiagnosing dyslexiamachine learningdeep learning |
| spellingShingle | Norah Dhafer Alqahtani Bander Alzahrani Muhammad Sher Ramzan Deep Learning Applications for Dyslexia Prediction Applied Sciences dyslexia detection dyslexia classification feature extraction diagnosing dyslexia machine learning deep learning |
| title | Deep Learning Applications for Dyslexia Prediction |
| title_full | Deep Learning Applications for Dyslexia Prediction |
| title_fullStr | Deep Learning Applications for Dyslexia Prediction |
| title_full_unstemmed | Deep Learning Applications for Dyslexia Prediction |
| title_short | Deep Learning Applications for Dyslexia Prediction |
| title_sort | deep learning applications for dyslexia prediction |
| topic | dyslexia detection dyslexia classification feature extraction diagnosing dyslexia machine learning deep learning |
| url | https://www.mdpi.com/2076-3417/13/5/2804 |
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