## 1 Introduction
Nevertheless, despite continuous attempts, there are not any reliable ASD therapy alternatives on the market. As such, pursuing the objective of research and development to improve treatment alternatives offers profitable prospects for market growth (see Figure 1 for more details). Offering children with ASD symptoms the proper therapy and treatment requires early discovery and diagnosis. The Centers for Disease Control and Prevention (CDC) report that, over the previous fifteen years, four and a half years have been the average age at which early indications of ASD are diagnosed. However, parents and other caretakers frequently become aware of issues at around two years old. As such, it is critical to apply efficient diagnostic and rehabilitation methods. There are two primary methods for identifying and monitoring kids with ASD: manual and automatic methods [12]. For the identification and diagnosis of ASD, automated systems using computer vision and image-based techniques in conjunction with conventional machine learning (ML) and deep learning (DL) are becoming increasingly common [13]. Additionally, manual techniques like observation- and interview-based methodologies are standard nowadays. The Childhood Autism Rating Scale (CARS), for example, uses 15 questions to diagnose ASD and uses scores to classify severity [14]. Interview-based systems such as the Growth-related Dimensional analysis, Diagnostic Interview (3 DI) [15], Autism Diagnostic Interview-Revised (ADI-R) [16], and Asperger Syndrome Diagnostic Interview (ASDI) require in-depth interviews with parents or other caregivers to diagnose ASD [17]. Similarly, the Gilliam Autism Rating Scale (GARS) uses 56 items divided into four categories to evaluate the severity of ASD [18]. However, this manual approach is not practicable for collecting data during daily activities since it primarily depends on expert opinion and behavioral observations. It takes a lot of time and money as well [19]. Researchers have created automated solutions to improve precision and effectiveness using the diagnosis process to overcome the shortcomings of conventional ASD diagnostic techniques [20; 21]. For example, conventional ML methods and computer vision have demonstrated potential for producing quick and efficient screening tools [22; 23; 24]. Meanwhile, DL approaches have outperformed traditional ML methods by automatically extracting characteristics and decreasing mistakes in identifying and diagnosing medical conditions, including ASD [21]. The capacity to analyze photos and videos for detecting, categorizing, diagnosing, and tracking ASD has increased dramatically in recent DL developments, significantly improving manual techniques [25; 26]. ML is a fast-growing Artificial Intelligence (AI) subfield that uses information to create highly accurate predictive models. ML encompasses diverse algorithms designed for classification, regression, and clustering [27]. These algorithms, such as Naive Bayes, support vector machines (SVM), logistic regression (LR), \(k\)-nearest neighbors (\(k\)NN), linear and polynomial regression (LPR), \(k\)-means, neural networks (NNs), and convolutional neural networks (CNNs), are adept at learning and interpreting intricate data features [27; 28]. Figure 1: Global ASD treatment market size (in USD billion) [11]. They are instrumental in predicting and classifying ASD across various age groups. Given the proper requirements, these techniques can predict survival rates, analyze behavior, study gaze patterns, and more, thereby facilitating early diagnosis of ASD [29]. Standardized behavioral evaluations, which can be drawn out and time-consuming, are commonly used to diagnose ASD. The goal of research on psychiatric neuroimaging is to find objective biomarkers that can help diagnose and treat brain-based diseases more effectively. By removing identifying characteristics from functional MRI (fMRI) data and passing them into classifiers, the literature has explored DL approaches to automate the diagnosis of ASD.