## 6 Discussion
It comprised 2D RGB images, aged from 2 to 8 years old. Facial images have been utilized by various authors as a research modality for the detection and classification of ASD using AI approaches such as ML and DL techniques, mainly focused on CNNs for extracting hidden features and the potential performance in the detection of ASD based on facial images [56]. For example, [PERSON] et al. [82] focused on the potential of facial features based on CNNs and employed transfer learning techniques, using MobileNetV2 and Hybrid VGG19 to improve the detection performance of ASD. However, there are some limitations to this modality; there is no information in the dataset related to clinical records, ASD severity, ethnicity, or socioeconomic condition of the children related to ASD. Further, the quality of images is not the best in terms of brightness, image size, and facial alignment. To acquire reliable predictions, the training data for DL algorithms should encompass a thoroughly inclusive dataset that includes all the details related to ASD. Moreover, magnetic resonance imaging (MRI), a noninvasive technique, has been widely utilized to analyze the brain's regional networks and provides structural information such as regional volumes, white matter, cerebrospinal fluid, and cortical thickness, all of which can help identify ASD using ML or DL techniques [99]. MRI scans are further separated into two categories based on scanning techniques: structural MRI (sMRI) and functional MRI (fMRI). Structural MRI (sMRI) scans are employed to assess the brain's anatomy and neurology and determine the brain's volume. Meanwhile, fMRI identifies variations in blood flow for functional connectivity analysis. However, this modality exhibits a few limitations as the samples utilized in various studies are insufficient or diverse enough to train their suggested techniques [97]. These datasets consist of data belonging to specific regions' populations, reducing their overall diversity, range, and usability. These datasets are pretty challenging to extract and then preprocess to make them usable and trained by different deep learning models or ML classifiers. At the same time, difficulties in understanding results, heterogeneity among ASD, and the impact of data quality on results are other limitations. If not conducted correctly, there might be a significant loss of accuracy in the results. For neuroimaging data to provide accurate and significant insights into ASD, these issues must be addressed appropriately. Similarly, an electroencephalogram (EEG) records the brain's electrical activity. Small sensors are applied to the scalp during this noninvasive process to pick up electrical impulses produced by the brain. A machine then records these signals, and a physician examines them [121]. Various authors have explored identifying ASD based on features extracted from EEG data using ML algorithms, and the efficiency of these methods depends closely on obtaining relevant features from the signals [124]. However, this modality has a few limitations, including the publicly available diverse datasets consisting of minimal samples with class imbalance issues and are inclined more toward specific gender and ethnicity [120]. Moreover, EEG datasets are quite challenging to record as they can cause discomfort to the individual even though the process is entirely painless. During recording, EEG signals can induce unwanted noise that can affect the ASD detection process and is quite challenging to remove. However, EEG presents various challenges in deciphering intricate brain patterns and the potential influence of confounding factors on EEG signals [53; 120; 122]. Eye tracking is an essential tool for examining a person's gaze position. It is a promising marker for ASD due to its speed, affordability, ease of analysis, and suitability for all age groups [112].