Computer-aided diagnostics of facial and oral cancer

Nowadays, cancer is the leading cause of death. It´s important to find oral cancer early when it can be treated more successful. Content-based image retrieval (CBIR) is a promising method for computer-aided diagnostics leading early diagnosis. In this paper, we perform FOCT (Facial and Oral Cancer Tracker), our new platform which assist surgeons in decisions regarding new cases by supplying visually similar past cases. This tool may guide diagnostic, treatment, management and monitoring oral cancer through comparison of long-term outcomes in similar cases. The primary goal of building such a data set is to supply surgeons with examples of oral tumor with a surgical intervention. Our application is based on a web interface, able to classify suspicious regions. This paper presents a novel feature selection techniques based on a hierarchical model, that can find what features best represent a given set of images. In order to improve the retrieval performance, a machine learning approach based on support vector machines (SVM) and relevance feedback strategies are investigated in this paper.