A set of methods suited to tackle the uncertainty, imprecision, of real systems, mostly nonlinear. These methods approximate real systems to obtain low computational cost, robust and tractable models.
Published in Chapter:
Image Based Classification Platform: Application to Breast Cancer Diagnosis
Paolo J. S. Gonçalves (Polytechnic Institute of Castelo Branco, Portugal & Technical University of Lisbon, Portugal), Rui J. Almeida (Erasmus University Rotterdam, The Netherlands), João R. Caldas Pinto (Technical University of Lisbon, Portugal), Susana M. Vieira (Technical University of Lisbon, Portugal), and João M. C. Sousa (Technical University of Lisbon, Portugal)
Copyright: © 2013
|Pages: 19
DOI: 10.4018/978-1-4666-3990-4.ch031
Abstract
The high number of exams that is done in healthcare institutions increases the medical doctors’ workload, leading to poor working conditions and the increase of wrong diagnoses. As consequence, an automatic system that can help medical doctors in diagnostic tasks is of major interest to any healthcare institution. The chapter proposes an Image Based Classification Platform suitable to help Medical Doctors diagnosing breast cancer, based on mammograms, i.e., to detect if a tumor is present in the image. The platform is twofold, i.e., in the first part the image descriptors are extracted from the image using image-processing algorithms. The obtained descriptors are used in the second part. The second part is related to classification, where computational intelligence methods are used to classify a given image, based on the descriptors obtained in the first phase. Texture analysis based on co-occurrence matrices are applied to obtain the descriptors from the MIAS database of mammograms. From these descriptors, fuzzy models, neural networks, and support vector machines are successfully used to classify the mammograms and obtain a diagnosis.