## 5 Discussion  
The use of facial landmarks has promise as a screening tool for autism spectrum disorder (ASD). Creating a concise and informative paradigm for ASD diagnosis is critical for overcoming the challenges of dealing with children. Therefore, developing such systems is a challenging task because of the intricacies of attentional behavior present in individuals with ASDs. Early diagnosis and intervention result in the most favorable outcomes for patients receiving treatment for ASDs. Hospitalization has traditionally been necessary to diagnose ASD in children; this process is not only expensive and time consuming, but it is also susceptible to expert prejudice [45]. In this work, we presented a method that is objective, usable, and effective for diagnosing children with ASD based on their facial expressions. Our concept reduces the gap between autism categorization and facial analysis, making automated autism categorization an alternative method that is more efficient in terms of both cost and time. Our deep learning model employed MobileNet as well as VGG-16 with various machine learning models in order to complete both of these objectives. The model was trained and validated using 2940 images; 80% of the data were used for training and 20% were used for testing. The data was obtained from children with autism, as well as children who did not have autism. According to the results of our study, a single image may be all that is required to make an accurate diagnosis of autism in children. It is possible that this diagnostic method could be used for a number of other illnesses as well. Our results reveal that the proposed model achieved an accuracy of 92%. The accuracy of MobileNet's results was evaluated using the ROC metric, with true-positives for normal and pathological classes presented on the \(y\)-axis and false-positives indicated on the \(x\)-axis. The results of the MobileNet model compared to different existing systems are presented in Table 10. Figure 18 presents the results of the measurements used to determine the ROC of the MobileNet model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Model** & **Dataset** & **Accuracy \%** \\ \hline
[10] & MobileNet & Kaggle dataset & 87 \\
[24] & Xception & Kaggle dataset & 90 \\
(2022) [46] & Xception & Kaggle dataset & 91 \\ Proposed & & Kaggle dataset & **92** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Significant results of ASD diagnosis system against existing diagnosis systems. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision \%** & **Recall \%** & **F1-Score \%** & **Support** \\ \hline Autism & 87 & 0.04 & 0.08 & 291 \\ Non-autistic & 0.51 & 0.99 & 0.68 & 297 \\ \hline Accuracy & & & 52.38 & & \\ \hline Macro avg & 69 & 52 & 38 & 588 \\ Weighted avg & 69 & 52 & 38 & 588 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of VGG-16 with KNN model. [PERSON] et al. [46] stated that they were able to achieve an accuracy of 87% using MobileNet on the new autism dataset. [PERSON] and [PERSON] used the Xception model, achieving an accuracy of 90%. A comparison of the accuracy reported by a number of studies is shown in Figure 19. [PERSON] and [PERSON] developed an exception model to detect ASD, using an old dataset that was considerably enhanced to help with classifying the algorithm with high accuracy. The proposed expert system increased the accuracy by 1% compared to recent, existing studies, due to the complexity of the dataset. This is the only standard dataset that has ASD images, which were collected from different individuals with different ages. Furthermore, the capturing of these images using different devices allowed the system to show the complexity of increasing its accuracy. If ASD is identified at an earlier age through the use of facial images, this will have a tremendous impact not only on the child, but also on their parents and the clinician.