This project is a master's thesis in computer science between Jan 2024 and June 2024 which handles sensitive personal data. Colorectal cancer (CRC) has a high mortality rate which can be decreased by early detection and intervention. Patients with inflammatory bowel disease (IBD) are at heightened risk of developing CRC and thus undergo regular surveillance, but recognition of pre-malignant (pre- cancerous) changes is difficult and inaccurate due to inflammation. These issues can further be exacerbated based on colonoscopy operator differences, such that many changes are missed. Recognition of pre-cancerous lesions can reasonably be improved by machine learning techniques by highlighting suspicious lesions for endoscopists during screenings. Novel imaging techniques such as chromoendoscopy further provide superior detection rates, and may be used in combination with AI. It shall now be investigated whether ML techniques can also be utilized in the support of colonoscopies in IBD patients, increasing accuracy and improving treatment outcomes.