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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 (ie, 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, preprocessing, results and limitations. Besides, we illustrate the various challenges and potential future directions relevant to anomaly detection in images.