Abstarct: Today, cancer is recognized as one of the leading causes of death in the world. Millions of people die because of cancer every year. Misdiagnosing some patients can lead to cancer and increase medical costs for individuals. Gastrointestinal cancers are one of the most common cancers. One of the most common diseases that cause cancer in the gastrointestinal tract is polyps. The polyps are usually cell masses that appear as an elliptical region inside the mucosal wall of the gastrointestinal tract. Misdiagnosing polyps on time can lead to cancer, given that the polyp diagnosis in the gastrointestinal tract is time-consuming according to the methods used for medical treatment. Therefore, it is necessary to provide automated systems for the diagnosis, and it is vital to perform Accelerate the curing process and prevent cancer. Today, artificial intelligence is used as one of the essential tools in various fields of medicine. This study intends to diagnose polyps using images related to the Gaussian Distribution and artificial intelligence algorithms. In this study, we first applied the Histogram Equalization algorithm to the images to extract better features. In the next step, using thresholding, we identify the probable polyp edges in the images with the help of thresholding. Then we identify the probable polyp central area with the help of probable polyp edges. Then, we extract the features from the probable polyp center using the Gaussian distribution, and we train a support vector machine to classify the polyp images from normal images. Finally, we segment the polyp areas in the detected images. According to the final results of the classification by the support vector machine, with 99.8% accuracy, was able to correctly predict the class of polyps. An essential point in the Research is to perform classification operations in a short time, which is suitable for real-time applications. Thus, in this study, it was shown that basic algorithms of artificial intelligence could be used for fast enough detection and high accuracy to detect polyps in wireless capsule endoscopy images.