## 4 Related Work in the ML and DL Domain
### Facial Image-Based Diagnosis of ASD
A year later, the study by [PERSON] and [PERSON] [80] determined whether facial traits taken from photos of autistic children may be used as a biomarker to distinguish autistic children from usually developing kids. The work uses a DNN model for accurate ASD classification and five pre-trained CNN models (MobileNet, Xception, EfficientNetB0, EfficientNetB1, and EfficientNetB2) with feature extraction. The 2936 colored 2D face photos in the dataset, which is publicly accessible and split into training, validation, as well as test sets with each grouping between autistic and non-autistic subgroups, are of children between the ages of two and 14, with the majority falling between the ages of two and eight. The CNN-based feature extraction tool captures complex face features important for categorization, demonstrating the model's ability to extract sophisticated features beyond human visual evaluation using digital filters. The year 2023 was critical for this type of application. [PERSON] et al. [81] improved the diagnosis of ASD by integrating DL with conventional techniques for detecting ASD based on the dataset of facial images using deep CNNs such as VGG16 and VGG19. The models were trained to achieve optimal accuracy in ASD identification using a batch size aggregating 12 over ten epochs. Additionally, the study conducted by [PERSON] et al. [82] used a dataset of facial images from Kaggle. It focused on ML and DL techniques to diagnose ASD using facial landmark analysis, such as LR, linear SVM, RF, DT, gradient boosting, MLP classifiers, and KNNs with deep CNNs, including MobileNetV2 and hybrid VGG19 based on transfer learning techniques to improve the performance of the models using Kaggle dataset with 2940 photos of children with and without autism. Finally, [PERSON] et al. [83] employed DL techniques and focused on improving the performance of ASD classification using MobileNetV2 and MobileNetV3 large models through a two-phase transfer learning using the dataset of facial images. This study showed improvements, supporting the two-phase transfer learning method regarding accuracy and Area Under the Curve (AUC). More recently, [PERSON] [84] addressed DL models; three pre-trained CNN models, VGG16, VGG19, and EfficientNetB0, were employed for feature extraction and classification of ASD using a dataset of facial images from Kaggle. The results revealed that DL models, especially EfficientNetB0, perform well in the classification of ASD. At this point, we would like to provide deeper comparative insights regarding the performance of convolutional neural networks (CNNs) and support vector machines (SVMs) in facial image analysis. Before CNNs became dominant, SVMs were considered state-of-the-art for many classification tasks. However, in recent years, CNNs have established themselves as the leading approach for object recognition in computer vision. One reason for their success is that convolutional layers can learn hierarchical and spatially-aware representations of input data, while fully connected layers subsequently map these representations to output classes. The superiority of CNNs is often attributed to several factors, including translation invariance, hierarchical feature learning, reduction in overfitting through techniques such as dropout and data augmentation, and efficiency in processing image data. By contrast, SVMs do not learn feature representations from data. Instead, they rely on a pre-specified kernel function to transform the data into a higher-dimensional space where it may become linearly separable. If the chosen kernel is appropriate, the SVM can perform well; if not, its performance degrades. This process can be seen as a form of \"educated guessing\". In contrast, CNNs can be interpreted as learning both the feature transformation (analogous to a data-driven kernel) and the classifier simultaneously. This capacity to adaptively learn representations makes CNNs particularly advantageous in complex or exploratory domains [85; 86].