Data fusion application for computer-assisted clinical endoscopic image analysis

Due to the complex nature of clinical manifestations, employing a single technique to detect different abnormalities may not necessarily yield accurate results. It is essential to develop an intelligent fusion approach to combine multiple data to arrive at an accurate final decision for clinical diagnosis. A new fusion approach is developed as an intelligent clinical data analysis technique for the cancer detection. It integrates decision-level-based fusion and multi-feature-based fusion. The results of new cases corresponding to accurate detection are fed back to refine appropriate parameters in a learning loop. The learning process is designed to estimate and reduce the error in the correct decision-making. The experimental results suggest that the newly proposed fusion approach has a better performance compared with some individual techniques, thus leading to enhanced accuracy of abnormality detection in cancer screening.