## 4 Related Work in the ML and DL Domain
### MRI-Based Diagnosis of ASD
After that, these characteristics are employed to train several ML models, such as NN, SVM, LDA, LR, and kNN, to diagnose ASD and the classification performance for ASD and TC participants. NN is implemented in PyCharm using Python, whereas LDA, LR, SVM, and kNN are executed in MATLAB. The same year, [PERSON] and [PERSON] [98] presented the Auto-ASD-Network model, which uses fMRI data to differentiate between individuals with ASD and healthy individuals. To increase classification accuracy and avoid overfitting, this model uses a multilayer perceptron (MLP) with two hidden layers and adds SMOTE for data augmentation. After being retrieved from MLP, the SVM classifier evaluates the features, and Auto-Tune Models (ATM) optimizes them. By interpolating between randomly chosen nearest neighbors, SMOTE is used to construct synthetic samples, increasing the training set's size. The C-PAC pipeline preprocesses the ABIDE initiative's fMRI data. The preprocessing steps include motion correction, slice timing correction, drift correction, nuisance signal removal, and voxel normalization. ABIDE's spatially limited spectral clustering techniques partition the brain into 200 areas. [PERSON] and [PERSON] [99] presented an ML system that uses brain volume and corpus callosum data to identify ASD. This method simplifies model training through selective feature extraction while achieving excellent recognition accuracy for ASD. The study also investigates the use of DL for neuroimaging data analysis, using the ABIDE-I dataset to build a VGG16 model. This dataset includes 573 healthy controls and 539 autistic persons from 17 countries. The effectiveness of ML in analyzing structural MRI data for ASD identification was demonstrated by the evaluation of several classifiers, comprising LDA, SVM (with an RBF kernel), RF, MLP, and kNN (with \(k=3\)). Additionally, in 2022, in the study by [PERSON] et al. [100], MRI data is used to classify ASD using a comprehensible DNN method. When the techniques were evaluated using the ABIDE dataset, it outperformed VGG16 and ResNet-50 and produced encouraging results. Preprocessing the data, isolating and finally integrating local ROIs, enhancing the data to prevent overfitting, and feeding the enhanced dataset through the LeNet-5 network. It utilized data processed by the Configurable Pipeline for the Analysis of Connectomes (C-PAC). The results justify the implementation of LeNet-5 throughout an ASD diagnostic assistance system by showing that adding additional MRI images to the annotated training set considerably improves performance. Using the ABIDE fMRI dataset, [PERSON] et al. [101] provided a DL strategy combined with the F-score feature selection method for ASD diagnosis. Their method finds functional connectivity characteristics in the ABIDE and intra-site datasets with great accuracy. ASD's path duration and cluster coefficient significantly decreased, as shown by feature analysis, suggesting a switch between small world and random network architecture. The model is tested using \(k\)-fold cross-validation, which splits the source data across \(k\)-equal subsections between training and testing. An autoencoder is used to remove lower-dimensional features from the model. Classification accuracy (ACC), sensitivity (SEN), and specificity (SPE) are performance measures that quantify the percentage of adequately categorized subjects, correctly recognized individuals with ASD subjects, and correctly identified healthy subjects, respectively. Similarly, [PERSON] and [PERSON] [102] used the ABIDE I dataset and applied a residual attention network integrated with a graph convolutional network to extract features from 4D brain scans. By using 10-fold cross-validation and the ABIDE I dataset, which comprises 800 individuals (389 with ASD and 411 controls), the model can attain a better accuracy rate. The training efficiency is increased by employing the Adam optimizer. The Adam optimizer was used to enhance the training accuracy or efficiency. More recently, [PERSON] et al.