Development of image processing techniques to extract visual features for the automatic classification of polyps in the bowel using Machine Learning algorithms.

The project was accomplished in collaboration with medical image processing company Medicsight and NHS expert colonoscopists. This project applied image processing, computer vision and machine learning techniques to endoscopic images, with the purpose of improving the characterisation and classification of large bowel polyps in real-time during colonoscopy.

I improved the classification accuracy on two datasets by a 2% and 5% respectively (up to 86.44% and 94.64%) by adding four new features to the existing five.

I also designed and implemented different approaches to narrow the margin of classification error by producing measurements of feature stability such as feature variability across the surface of polyps.

I used several classification techniques, most importantly support vector machines (SVMs) and linear discriminant analysis (LDA).