[Paper] Classification of Gastric Cancer Risk From X-ray Images Based on Efficient Image Features Related to Serum Hp Antibody Level and Serum PG Levels

Abstract

In this paper, a fully automatic gastric cancer risk classification method with the aim of constructing a computer-aided diagnosis (CAD) system is presented. Two-stage classification is used in the proposed method for determining gastric cancer risk. In the first stage, the proposed method detects H. pylori-infected patients, i.e., detection of patients who have gastric cancer risk, and the proposed method classifies the level of gastric cancer risk, i.e., high or low, from H. pylori-infected patients in the second stage. In each stage, we derive new image features that are closely related to values of blood examination via kernel canonical correlation analysis. The introduction of these new image features provides classification improvement in each stage, and it is the main contribution of this paper. Consequently, accurate classification becomes feasible by the proposed method. Experimental results obtained by applying the proposed method to real X-ray images show that our method outperforms several comparative methods.