Capsule endoscopy is a new technology developed for diagnosing small bowel. The patient swallows a small capsule and it captures a video of the whole way till expulsion. Due to long videos, it takes a great effort of experts to analyze them carefully. AI which has proved its usefulness in various fields of medical sciences can be adopted to do automatic detection of anomalies to help in accurately fastening the diagnosis. In this project, we aim at detecting ulcers, bleedings, and polyps from the tagged videos of 150 patients provided by Shahid Beheshti University of Medical Sciences using interpretable machine learning models. Each video is nearly O(60,000-80,000 frames). The system should also do automatic measurements of the sizes of anomalies and prepare a summarized report and help in speeding up the operations of experts. In addition to these challenges, only the frames containing a lesion are marked but the exact locations of the lesions are not available. This puts the problem in the category of weakly supervised training in which finding the exact boundaries are important for the automatic measurements. There are also many additional challenges regarding this project including highly imbalanced data as the lesions are only present in a few frames, highly correlated images, the problem with the large number of frames per sample requiring a fast and efficient solution, and the three-dimensional input. The previous studies have not considered detecting polyps, lesions, and bleeding altogether in one model. They are also trained on videos from devices other than the ones we are using in Iran and so may not be applicable. Therefore it is essential for us to prepare a CADx system to detect these anomalies and notify them in capsule endoscopy videos.