Anomaly Detection in Image Processing using Artificial Intelligence

Any method that looks for samples that deviate from anticipated patterns is sometimes referred to as anomaly detection. Many outlier finding methods are established on the types of irregularities, various types of data labels that are available, and the applications. The aim of this paper is to give an overview and systematic evaluation of outlier finding research. We believe that the paper contributes to a better understanding of the numerous fields in various types of research done on this subject. The approaches for anomaly detection have been separated into many groups. We discuss the pros and cons of each category and look at how computationally complex the various methods are in terms of real-world application domains. We concluded the paper by providing research gaps and constraints we faced while applying artificial intelligence algorithms in practical applications. This review paper examines several outlier detection and treatment techniques in images, as well as gives better performance of various AI approaches.