# Deep Learning Applications for Dyslexia Prediction
## 4 Research Results
### Classification and Performance of Deep Learning Models  
As illustrated in traditional studies, ML methods have been used in the prediction of dyslexia, and recent advances in ML methods (DL) have resulted in outstanding accuracy. The use of DL models in dyslexia prediction varies based on the dataset used. For example, CNN models are appropriate for handling unstructured data, as shown in all studies \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Dataset** & **Feature Extraction Method and Selected Features** \\ \hline
[44] & EOG & The study used a 1D CNN model that generated feature maps through operations in layers. These feature maps contained significant features from the vertical and horizontal EOG signals, allowing the differentiation of dyslexic and normal readers. \\ \hline
[12] & Features of the phonological and cognitive brain systems related to gray matter, white matter, and cerebrospinal fluid were extracted from fMRI utilizing CAT12 implemented in MATLAB. \\ \hline
[38] & The study used FDT, BET, and TBSS to extract FA from DTS signals, and used two tools (BET and FEAT) to elicit features from fMRI. These two features (activation pattern and FA) are associated with speech, language, and lexical decisions. \\ \hline
[37] & MRI and fMRI & After preprocessing fMRI dataset, SMP12 has been implemented in MATLAB 2018b to extract cognitive features pertaining to grey and white matter and the volumetric biomarkers of cerebrospinal tissues. \\ \hline
[39] & This study was considered unique due to its visualization of features, which differentiated dyslexic readers from normal ones, such as activation patterns of anterior right-hemisphere prefrontal areas as well as activation patterns in the left occipital and inferior parietal areas that distinguished groups based on brain networks related to lexical and phonological processes in reading. The feature extraction has been carried out in the layers of the LeNet-5 model. \\ \hline
[25,26] & These studies used behavioral and cognitive biomarkers (picture patches of handwriting features) to differentiate between dyslexic and normal readers. A CNN model extracted high features from processed handwriting images when implemented in Python. \\ \hline
[28] & Hindi words have some prodigious features, such as diacritics (matras), conjoined consonants, and killer strokes (halants). The study used patches of handwriting features to differentiate between dyslexics and normal readers, which have been extracted in the CNN model \\ \hline
[27] & Handwriting & The study used an OCR technology to identify letters in handwriting images. The rectified characters are displayed in the command box after picture extraction and character recognition, manually the total of correct detection was been calculated. \\ \hline
[4,29] & These studies used a CNN model to extract features and predict dyslexia from handwriting images. The preprocessed handwriting images were fed to the CNN model, and a feature map was created through the convolution layer, which contained highly informative features. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._presented in this paper except [30], which dealt with numerical data. Although some studies used ANNs to deal with EGG datasets and achieved high accuracy (96%) [33], others achieved lower accuracy (78%) [34]. In addition, some studies that adopted handwriting datasets used CNN models and reported accuracies between 86% and 95% [4; 28; 29], while others achieved low accuracy (maximum 77%) [22; 23; 25]. Typically, the datasets were partitioned to train and test the prediction models. Most of the data (approximately more than or equal to 70% of all the data) were utilized for training, and the smaller part was allocated for testing model. Some studies allocated part of the dataset (might be 10% or 20%) as a validation dataset, which is a sample of data that is restricted from the process of the training model. These data are used to provide an estimate of the model's skill while the hyperparameters are being tuned.