Deep Learning based Anomaly Detection in Images: Insights, Challenges and Recommendations

Ahad Alloqmani,Fawaz Alsolami,Asif Irshad,Yoosef B

doi:10.14569/ijacsa.2021.0120428

Ahad Alloqmani, Fawaz Alsolami + Show 2 more

Open Access

https://doi.org/10.14569/ijacsa.2021.0120428

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Abstract

Deep learning-based anomaly detection in images has recently been considered a popular research area with numerous applications worldwide. The main aim of anomaly detection (i.e., Outlier detection), is to identify data instances that deviate considerably from the majority of data instances. This paper offers a comprehensive analysis of previous works that have been proposed in the area of anomaly detection in images through deep learning generally and in the medical field specifically. Twenty studies were reviewed, and the literature selection methodology was defined based on four phases: keyword filter, publish filter, year filter, and abstract filter. In this review, we highlight the differences among the studies included by considering the following factors: methodology, dataset, prepro-cessing, results and limitations. Besides, we illustrate the various challenges and potential future directions relevant to anomaly detection in images

Highlights

Identifying examples that deviate from what is typical or expected is the primary goal of anomaly detection and known as outlier detection [1]
The results indicate that Deep Autoencoding Gaussian Mixture Model (DAGMM) exceeds state-of-the-art anomaly detection methods with a 14% improvement based on the standard F1 score
The results demonstrate that this method can detect anomaly detection at a very early stage with 72.7% and www.ijacsa.thesai.org at a late stage with 89.4% in terms of area under the curve (AUC)

Summary

Introduction

Identifying examples that deviate from what is typical or expected is the primary goal of anomaly detection and known as outlier detection [1]. Many researchers tended to employ deep learning to detect abnormalities in images, due to the proliferation of deep neural networks, with unprecedented results across various applications. It can deal with complicated features such as regions of interest points by examining every pixel in an image [4] [5]. We briefly explain the elements of the context of this review (i.e., anomaly detection, deep learning, and automated medical image diagnosis). Known as outlier detection, is defined as the process of identifying data instances that deviate www.ijacsa.thesai.org. Given that “O3”, “O1”, and “O2” are considered to be anomalies They occur due to data errors but sometimes indicate a new basic process that was not previously known [5]. Detection plays an increasingly important role and is highlighted in different communities, including machine learning, computer vision, and data mining [4]

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