# Deep Learning Applications for Dyslexia Prediction
## 4 Research Results
### Data Preprocessing  
Datasets related to dyslexia need to be processed and prepared before used in DL techniques. The goal of this task is to enable the classifier to elicit the most related interpretable features from the preprocessed dataset. There are several objectives of preprocessing processes, including modulation, tissue segmentation, data normalization, smoothing, alignment with a particular image template, and data normalization [19]. The study by [PERSON] and [PERSON] [30] was different from the other studies in this review, as it utilized manual data preprocessing methods. One study [32] applied decimation and STFT to the fMRI signals, which were filtered using a bandpass procedure to remove noises and irrelevant artifacts.  
Figure 4: Distribution of articles that used DL methods based on datasets from 2010 to 2022.  
In addition, an FSL instrument, the FMRIB software library, was employed for processing DTI and fMRI [38]. [PERSON] and [PERSON] changed all of the T1w neuro-images (images of the brain) into a format that could be used by FreeSurfer software [12]. Then, the intensity of the images was adjusted so that they all had the same brightness and contrast. The normalization of intensity was based on histograms. A FSL FNIRT software instrument was utilized for non-rigid registration in the brain template of MNI152, and a Gaussian filter was used to minimize noise. In [37], the preprocessing step included a DICOM to NIFTI conversion, taking the \"Digital Imaging and Communications in Medicine\" file and converting it into a \"Neuroimaging Informatics Technology Initiative\" file. This conversion was necessary in order to use the data in SPM (Statistical Parametric Mapping) software to analyze the brain image data. Normalization and smoothing were applied in addition to the adjustment of head motion. In another study [39], the preprocessing of the task-based fMRI involved the correction of slice time and motion, smoothing, and normalization to a template of a voxel. [PERSON] and [PERSON] [33] used standard algorithms to filter irrelevant records and noise from the dataset. In addition, they removed noise from EEG scans using a Fourier transform algorithm. Other authors used various wavelet transformation approaches to convert EEG scans into low-pass and high-pass filters [34]. Independent component analysis was used in the preprocessing of EEG signals to remove artifacts due to recording eye blinking signals along with EEG signals [35]. The studies that used the eye-tracking dataset to predict dyslexia focused on eliminating blinks and processing missing data [40; 41; 43]. The study that used EOG signals utilized the Butterworth bandpass filter to eliminate noise, followed by EOG segmentation. The aim of segmentation was to balance the dataset (dyslexic and healthy groups). With regard to a handwriting dataset, [PERSON] et al. [25; 26] applied the DeepWriter concept [45] to create 50 random patches of handwriting features from each image in the handwriting dataset. [PERSON] and [PERSON] [28] utilized the same concept to process Hindi letter datasets. Some studies included different steps for processing unstructured data (handwriting images), as shown in [27], including RGB to grayscale conversion, maximally stable extremal region, canny edges detector, stroke width filter, and morphology as a final step. [PERSON] et al. [4; 29] employed the same dataset. For preprocessing, they interchanged the foreground and background to minimize computational overhead when an image had a lower black point (value 0) than white point (value 1), which required more memory and power consumption in training the image. In addition, they cropped handwriting images to the actual writing part, thus resizing the images.