## 4 Related Work in the ML and DL Domain
### MRI-Based Diagnosis of ASD  
Strong magnets and radio waves are used in MRI to produce precise images of the body. MRI works similarly to a powerful camera that can see through muscle and skin, providing doctors with incredibly detailed images of organs, tissues, and the brain [90]. Since MRIs do not use radiation, they are safer than CT scans and X-rays. Because of this, they are ideal for evaluating soft tissues, such as the brain and muscles, which aid in diagnosing everything from malignancies to brain damage [91; 92]. \begin{table}
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**Citation** & **Dataset** & **Technique** & **Reported Metrics** & **Limitations** \\ \hline \multirow{4}{*}{[78]} & Self-collected dataset of Chinese children with ASD (private) & \multirow{4}{*}{\(k\)-means and SVM} & Accuracy: & 88.51\% & Limited data sample, algorithmic limitations, specificity: & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & &  
AI-based systems can decipher the complex information concealed in MRI images, particularly those built on ML, DL, and computer vision. These scans capture the structure and functionality of the brain, and AI decodes the intricate patterns inside them like a codebreaker. AI may detect minute variations in brain areas and their connections, frequently connected to ASD, by training on enormous volumes of MRI data. AI can recognize these subtleties that human specialists could overlook. This makes it possible to diagnose ASD more accurately and comprehend its brain roots on a deeper level. Additionally, MRI is a noninvasive, accurate method of making an early diagnosis of ASD in collaboration with medical specialists, which improves treatment strategies and provides insightful information about this complex disease [93; 94]. For instance, [PERSON] et al. [95] used a resting-state functional MRI (rs-fMRI) dataset gathered by the Autism Imaging Data Exchange (ABIDE I), which included 530 typical controls (TC) and 505 people with ASD. The information collected from seventeen distinct imaging locations was utilized to train a classification model. The researchers used denoising autoencoders to improve model accuracy on new data, which recreated inputs from faulty copies. For the unsupervised pre-training phase, two stacked autoencoders were used to minimize the dimensionality of the data and maximize reconstruction loss. The autoencoder weights were merged into the multilayer perceptron (MLP), the final model, which was then fine-tuned to reduce prediction errors. The output layer of the MLP yielded the likelihood that an input image would be categorized as either TC or ASD. In addition, the work conducted by [PERSON] et al. [96] used fMRI scans to identify biomarkers for ASD using a dual-phase technique. Initially, spatial-temporal data from 4D fMRI, compressed to \(32\times 32\times 32\) dimensions, is trained into a DNN classifier (2 CC3D). The classifier combines dropout and L2 regularization to avoid overfitting with convolutional and fully connected layers. The technique uses task-based fMRI images processed by motion correction, slice timing correction, brain extraction, spatial smoothing, and high-pass filtering on participants with ASD and control subjects doing a pointing task. Sliding window analysis over the fMRI time dimension produces 144 3D volume pairings per participant during region segmentation, which is carried out using the AAL atlas. The method's effectiveness in distinguishing ASD-related brain characteristics amongst ASD and experimental results demonstrate healthy controls. Just a year later, [PERSON] et al. [97] used the ABIDE dataset, which comprises structural MRI, rs-fMRI, and phenotypic data from 1112 participants (539 ASD and 573 TC), to diagnose ASD and 264 raw brain features derived from the eigenvalues of the Laplacian matrix of the brain network plus three network centrality features. These 264 features were reduced to 64 using a feature selection technique.