Multi-agent Diagnostic System for Respiratory Disease

Advances in the area of computer science and artificial intelligence (AI) have a tremendous impact on the interpretation of medical sounds and images. Computer-Aided Diagnosis (CAD) aims to provide a computer output as a second opinion in order to assist physicians in the detection of abnormalities, quantification of disease progress and differential diagnosis of lesions. This thesis investigates a number of computational intelligence techniques in the detection of respiratory disease. Firstly, A Computer-based sound recognition system has been proposed for the diagnosis of pulmonary disorders. Secondly, a content-based image retrieval system has been presented to retrieve chest Computed Tomography (CT) images that are pathologically similar to a given example. Eventually, the thesis also investigated the integrating artificial intelligence techniques in the diagnosis of respiratory disease. Accordingly, a computer based system combining sound and image processing has been proposed for the diagnosis of respiratory disease. This system categorizes chest diseases based on information extracted from a patient's respiratory sound signal and his chest CT image.