A Systematic Review of Research Dimensions Towards Dyslexia Screening Using Machine Learning

Jan, Tabassum Gull; Khan, Sajad Mohammad

doi:10.1007/s40031-023-00853-8

Cited by 13 publications

(7 citation statements)

References 42 publications

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“…They have also explained it by reviewing the clinical observations and research data perspectives in Mather and Schneider (2023) and Wagner et al (2023), evaluated with the help of Bayesian identification model and included set of predictors. In Alqahtani et al (2023), Ahire et al (2023), Jan and Khan (2023), and Parmar and Paunwala (2023), the authors have worked on detection and categorization of dyslexia with a strong focus on implementing ML methods, like deep learning and electroencephalogram data analysis, in the detection and categorization of dyslexia.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Dyslexia Detection: A Comprehensive CNN Approach with Handwriting Analysis and Benchmark Comparisons

Aldehim,

Rashid,

Alluhaidan

et al. 2024

Journal of Disability Research

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Dyslexia is a complex learning disorder that affects neurological nerves in the brain and makes reading and writing difficult; therefore, early diagnosis for effective interventions becomes important. This study demonstrates how quickly dyslexia can be identified by introducing an advanced convolutional neural network model developed for detecting dyslexia through image-based handwriting analysis. The need for early identification is informed by the fact that dyslexia may, in certain cases, lead to poor academic performance and emotional imbalance among learners. This method of using deep learning outperforms all other established conventional methods due to inherent sensitivity in classifying handwritings of dyslexics from those of normal individuals. The artificial intelligence (AI)-supported technology has the highest training accuracy of 99.5% proving its ability to capture subtle features related to the presence of dyslexic tendencies. Consequently, it records a testing accuracy of 96.4%, thereby confirming its efficacy under practical circumstances. In addition, the model also shows a good F1-score of 96 which indicates that it can achieve a balanced precision versus recall trade-off unlike other state-of-the-art approaches. The obtained results of the proposed methodology were compared with those of previous state–of-the-art approaches, and it has been observed that the proposed study provides better outcomes. These detailed performance indicators point toward the potential usefulness of AI-based methods in identifying dyslexia thus informing appropriate interventions on time and targeted assistance to the patients suffering from this disease.

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Section: Related Workmentioning

confidence: 99%

Deep Learning for Dyslexia Detection: A Comprehensive CNN Approach with Handwriting Analysis and Benchmark Comparisons

Aldehim,

Rashid,

Alluhaidan

et al. 2024

Journal of Disability Research

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“…The open nature of the Internet age and the promotion of lifelong education (Blaschke, 2021;Huang, 2021) have placed dyslexic learners in the domain of online education, but some learners with dyslexia are not comfortable with online education (Jan 2023), and dyslexia negatively affects their online learning outcomes (Fallon, 2020). Related theoretical studies have focused on developing online learning as an effective intervention for dyslexia, ignoring the impact of dyslexia on online learning outcomes (Zuppardo et al, 2021).…”

Section: Objectivementioning

confidence: 99%

The Impact of Dyslexia on the Effectiveness of Online Learning: A Systematic Literature Review

Liu

Costa

Yasin

2023

SMC

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Dyslexia can have an impact on online learning outcomes. However, few studies have examined the association between dyslexia and online learning effectiveness. This systematic review focused on the effects of dyslexia on online learning effectiveness to conduct research in three major categories: analysis of impact, analysis of dyslexia on online learning, and analysis of interventions. A screening of two bibliographic databases identified 37 articles that met the inclusion criteria. The study determined that dyslexic learners are uncomforted with online learning and suffer from frustrated self-confidence and decreased academic performance in the learning process. It even affects the learners' evaluation of themselves and causes a decrease in self-efficacy. Among these, research on influencing factors can be divided into two dimensions: internal and external factors. Dyslexic learners are influenced by both the type of dyslexia and their psychological characteristics, as well as by teachers, teaching strategies, online educational environments, and educational media. These influences provide intervention strategies, such as developing customized online learning systems for dyslexic learners and exploring interventions in telerehabilitation medicine. However, no intervention strategies involve adjustments to the internal psychology of dyslexic learners and external support systems. Therefore, more research is needed to explore the differential impact of dyslexia on online learning and to understand the factors that produce this impact to provide a theoretical basis and direction for the generation of instructional strategies for dyslexics and the adaptation of online learning for dyslexics.

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“…With FMRI, researchers can track the brain’s blood flow in real time, revealing actively engaged regions in completing various tasks ( Usman et al, 2021 ). Researchers discovered dyslexia-related brain regions by comparing dyslexic and non-dyslexic neural activity during reading tests ( Jan & Khan, 2023 ). These discoveries may lead to a deeper understanding of the neurological processes behind dyslexia.…”

Section: Introductionmentioning

confidence: 99%

“…Research studies ( Ahire et al, 2022 ; Jan & Khan, 2023 ; Marimuthu, Shivappriya & Saroja, 2021 ; Perera, Shiratuddin & Wong, 2018 ; Usman et al, 2021 ; Velmurugan, 2023 ) systematically reviewed DD techniques based on DL approaches. Spoon, Crandall & Siek (2019) used children’s handwritten images to predict dyslexia.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-driven dyslexia detection model using multi-modality data

Alkhurayyif,

Wahab Sait

2024

PeerJ Computer Science

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Background Dyslexia is a neurological disorder that affects an individual’s language processing abilities. Early care and intervention can help dyslexic individuals succeed academically and socially. Recent developments in deep learning (DL) approaches motivate researchers to build dyslexia detection models (DDMs). DL approaches facilitate the integration of multi-modality data. However, there are few multi-modality-based DDMs. Methods In this study, the authors built a DL-based DDM using multi-modality data. A squeeze and excitation (SE) integrated MobileNet V3 model, self-attention mechanisms (SA) based EfficientNet B7 model, and early stopping and SA-based Bi-directional long short-term memory (Bi-LSTM) models were developed to extract features from magnetic resonance imaging (MRI), functional MRI, and electroencephalography (EEG) data. In addition, the authors fine-tuned the LightGBM model using the Hyperband optimization technique to detect dyslexia using the extracted features. Three datasets containing FMRI, MRI, and EEG data were used to evaluate the performance of the proposed DDM. Results The findings supported the significance of the proposed DDM in detecting dyslexia with limited computational resources. The proposed model outperformed the existing DDMs by producing an optimal accuracy of 98.9%, 98.6%, and 98.8% for the FMRI, MRI, and EEG datasets, respectively. Healthcare centers and educational institutions can benefit from the proposed model to identify dyslexia in the initial stages. The interpretability of the proposed model can be improved by integrating vision transformers-based feature extraction.

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A Systematic Review of Research Dimensions Towards Dyslexia Screening Using Machine Learning

Cited by 13 publications

References 42 publications

Deep Learning for Dyslexia Detection: A Comprehensive CNN Approach with Handwriting Analysis and Benchmark Comparisons

Deep Learning for Dyslexia Detection: A Comprehensive CNN Approach with Handwriting Analysis and Benchmark Comparisons

The Impact of Dyslexia on the Effectiveness of Online Learning: A Systematic Literature Review

Deep learning-driven dyslexia detection model using multi-modality data

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