# Deep Learning Applications for Dyslexia Prediction
## 5 Discussion and Challenges  
Dyslexia disorder is a learning difficulty that hinders an individual's learning skills. Commonly, this disorder is pointed to as a neurological trouble that complicates remembering and addressing information in dyslexics. Artificial intelligence methods (ML and DL) have been vastly utilized for the prediction of dyslexia over recent years. The detection processes involved successive steps starting from data acquisition and preprocessing, then the extraction and selection of features, and thereafter the training process, to the model estimation process. The prediction of dyslexia disorder based on ML, particularly DL methods, was successful in the studies included in this review, achieving high accuracies of 94.8% [34], 94.8% [35], 95.34% [25], and 96.6% [36], respectively. CNN models were the most commonly used models for dyslexia prediction and showed the highest accuracy. However, there were some challenges. The acquisition of dyslexia datasets is not easy. The studies that utilized handwriting datasets encountered difficulties in acquiring data from dyslexia children [25, 26] in addition to their limited size. Furthermore, as shown in Table 3 (column three), the number of subjects did not exceed 185, but DL models require large-scale data to improve predictive model performance. In addition, the high cost of brain-imaging technology limits its usage, and, therefore, some studies tended to use cheaper EEG and eye-tracking systems. Often, CNN models lead to overfitting problems in the training phase, where the accuracy of the model is high with the training set but low with the testing set due to involving a large number of parameters [47]. Moreover, the dyslexia datasets suffer from their small scale, leading to overfitting. To avoid this, several techniques have been suggested, such as:  
* The dropout technique that commonly employs the method of generalization. Throughout each training period, the neurons are randomly eliminated. In doing so, the power of feature selection is divided uniformly over the entire group of neurons, and the model is forced to learn multiple independent features [48]. * Data augmentation: Training the model on a substantial amount of data is the simplest method for avoiding overfitting [49]. Several strategies are employed to augment \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Datasets** & **DL Model** & **No. of Subjects** & **Performance** \\ \hline
[4] & Handwriting image & CNN & \
\begin{tabular}{c} Normal = 78,275 letters \\ Dyslexic = 52,196 letters \\ \end{tabular} &
\begin{tabular}{c} Accuracy of CNN1: 86\% \\ Accuracy of CNN2: 87\% \\ Accuracy of CNN3: 86.5\% \\ Accuracy of LeNet-5: 86\% \\ Accuracy of LeNet-5: 86\% \\ Accuracy: 95.34\% \\ \end{tabular} \\ \hline
[40] & Eye tracking & CNN &
\begin{tabular}{c} N = 185 \\ 88 low risks \\ 97 high risks \\ \end{tabular} & Accuracy: 96.6\% \\ \hline
[39] & fMRI & CNN &
\begin{tabular}{c} N = 32 children \\ 16 typical readers \\ 16 dyslexic readers \\ \end{tabular} & Accuracy: 94.8\% \\ \hline
[41] & Eye tracking & CNN &
\begin{tabular}{c} N = 185 \\ 97 dyslexics \\ 88 non-dyslexics \\ \end{tabular} & Accuracy of CNN: 87\% \\ \hline
[43] & Eye tracking & CNN (VGG 16) &
\begin{tabular}{c} N = 30 \\ 15 dyslexics \\ 15 normal \\ \end{tabular} & Accuracy: 87\% \\ \hline
[44] & EOG & CNN &
\begin{tabular}{c} N = 33 \\ 20 dyslexics \\ 13 normal \\ \end{tabular} & Accuracy for horizontal channel \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._the size of the training dataset, such as cropping, translation, and rotation. Rotation and noise injection techniques have been used in the study [29]; they contributed to enlarging the training size dataset and solved the imbalanced class problem. The dataset preprocessing step is a critical step, and the formative features extracted from datasets using DL models, especially CNNs, depend on good processing, which affects the classification accuracy.