Detection of ulcers in wireless capsule endoscopy (WCE) images is a crucial task in the field of gastrointestinal medicine. Gastrointestinal ulcers can be indicative of various conditions, and their early detection is essential for effective treatment. Manual analysis of WCE images is time-consuming, leading to high healthcare costs. In this review, we examine several approaches proposed for ulcer detection in WCE images. These approaches include both traditional machine learning techniques and deep learning methods. We also discuss available datasets for training and validating ulcer detection algorithms. A review of the literature shows that deep learning has proved to be more successful than traditional machine learning tools.