Analysis of medical image is new technique to solve the medical problems by examine the images that has been generated through some detailed clinical practice. For better clinical diagnosis, the goal is to extract information in a way that is both affective and efficient. Abdominal bleeding, ulcers, tumors, celiac disease, ulcerative colitis, and other gastrointestinal disorders are difficult to diagnose due to the uncertainty of accessing a volute condition in the human body. WCE is a non-invasive, painless, and patient-controlled gastrointestinal tract examination. Although using deep learning models to detect problems in WCE photos enhances detection accuracy, model training necessitates a large volume of labeled data. These deep models, on the other hand, are difficult to describe and do not incorporate expert input into the model decision-making process. Deep learning-based machine learning breakthroughs have improved the ability to categories, detect, and quantify patterns in medical images. Deep learning approaches are quickly being applied in medical imaging to increase performance in a variety of medical applications. Due to recent advancements in the field of biomedical engineering, medical image analysis has become one of the most important research and development areas. The use of machine learning algorithms for the processing of medical images is one of the reasons for this progress. Deep learning has been successfully applied as a machine learning technology, allowing a neural network to learn information automatically. This contrasts with methods that rely on traditional handcrafted elements. The selection and computation of these characteristics are a difficult task. Deep convolution networks, a type of deep learning approach, are widely employed in medical image processing. With these considerations in mind, the purpose of this research is to see if employing semi-supervised deep learning models over supervised deep learning methods for detecting and categorizing anomalies in WCE has any advantages. This paper provides a comprehensive overview of different deep learning algorithms for anomaly detection and localization methodologies utilized in WCE photographs in terms of performance, complexity, and dataset quality. The findings highlight existing research constraints in wireless capsule endoscopy image processing as well as future research prospects.