title: Cholangiocarcinoma--an automated preliminary detection system using MLP.
abstract: Cholangiocarcinoma, cancer of the bile ducts, is often diagnosed via magnetic resonance cholangiopancreatography (MRCP). Due to low resolution, noise and difficulty is actually seeing the tumor in the images, especially by examining only a single image, there has been very little development of automated systems for cholangiocarcinoma diagnosis. This paper presents a computer-aided diagnosis (CAD) system for the automated preliminary detection of the tumor using a single MRCP image. The multi-stage system employs algorithms and techniques that correspond to the radiological diagnosis characteristics employed by doctors. A popular artificial neural network, the multi-layer perceptron (MLP), is used for decision making to differentiate images with cholangiocarcinoma from those without. The test results achieved was 94% when differentiating only healthy and tumor images, and 88% in a robust multi-disease test where the system had to identify the tumor images from a large set of images containing common biliary diseases.
authors.affiliation: Global School of Media, Soongsil University, Seoul, South Korea. loges@ieee.org
authors.forename: Rajasvaran
authors.initials: R
authors.lastname: Logeswaran
mesh_terms: D000465:Algorithms; D001650:Bile Duct Neoplasms / Q000175:diagnosis*; D001653:Bile Ducts, Intrahepatic / Q000473:pathology*; D018281:Cholangiocarcinoma / Q000175:diagnosis*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D016571:Neural Networks, Computer