Copyright: © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Science and Technology Print ISSN: 2395-6011 | Online ISSN: 2395-602X (www.ijsrst.com) doi : https://doi.org/10.32628/IJSRST2310160 446 Deep Learning Techniques for Early Detection of Autism Spectrum Disorder - Systematic Study Asif Mohamed H B 1* , Dr. Md. Sameeruddin Khan 2 , Dr. Mohan K G 3 , Dr. Parashuram Baraki 4 1 Research Scholar, School of CSE, Presidency University, Bengaluru, Karnataka, India 2 Professor & Dean, School of CSE, Presidency University, Bengaluru, Karnataka, India 3 Professor, Department of CSE, GITAM University, Bengaluru, Karnataka, India 4 Professor, Department of CSE, Smt. Kamala and Sri Venkappa M Agadi College of Engineering and Technology, Lakshmeshwar Dist : Gadag, India Article Info Publication Issue Volume 10, Issue 1 January-February-2023 Page Number 446-450 Article History Accepted: 03 Feb 2023 Published: 16 Feb 2023 ABSTRACT This paper narrates the various categories of image classification techniques. Image classification is an important tool for extracting information from digital images. The purpose of this research article is to summarize information on several image classification techniques. Keywords: Deep Learning Techniques, Autism Spectrum Disorder, Image Preprocessing, Object Detection, Feature Extraction I. INTRODUCTION The process of classifying pixels into a finite set of individual classes based on their data values is known as image classification. A pixel is assigned to a particular class if it satisfies a particular set of rules for fitting into a particular class. Class may be known or unknown. Image classification techniques can be categorized into parametric and nonparametric classification, supervised and unsupervised classification, hard and soft classification. Image classification is the classification of images into one of several predefined classes. II. METHODS AND MATERIAL 2.1 Image Preprocessing - The purpose of this process is to suppress unwanted distortions and improve some important image features so that computer vision models can benefit from this improved data. It is to improve the image features by making it possible to receive. 2.2 Object Detection - Detection refers to locating an object that is, segmenting an image and locating an object of interest. 2.3 Feature Extraction and Training – The use of deep learning methods to identify the most interesting patterns in the image, possibly specific to a particular class, and later evolve the model to identify different classes. It is an important step to identify the features.