Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian Decomposition and Bayesian Neural Networks |
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| Affiliation: | 1. Universidad Autònoma de Bucaramanga, Colombia;2. Electrical, Electronics and Telecommunications Engineering School, Universidad Industrial de Santander, Colombia;3. Departament de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Spain;4. Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain;1. Instituto para el Desarrollo Tecnológico y la Innovación en Comunicaciones (IDeTIC), Universidad de Las Palmas de Gran Canaria, Despacho D-102, Pabellón B, Ed. de Eletrónica y Comunicaciones, Campus de Tafira, 35017 Las Palmas, Spain;2. Escuela de Biología, Universidad de Costa Rica, Costa Rica;3. Systems Engineering and Automation Department, Universidad del País Vasco/Euskal Herriko Unibertsitatea, Spain;1. Informatics Center — Federal University of Pernambuco (UFPE), Pernambuco, Brazil;2. Federal University of Bahia (UFBA), Bahia, Brazil;1. Faculty of Computer Science, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, D-39106 Magdeburg, Germany;2. Institute for Community Medicine, Ernst-Moritz-Arndt University Greifswald, Walther-Rathenau-Straße 48, D-17475 Greifswald, Germany;3. Institute for Diagnostic Radiology and Neuroradiology, Ernst-Moritz-Arndt University Greifswald, Sauerbruchstraße, D-17487 Greifswald, Germany;1. Khalifa University of Science, Technology and Research, P.O. Box 127788, Abu Dhabi, United Arab Emirates;2. Etisalat BT Innovation Center, P.O. Box 127788, Abu Dhabi, United Arab Emirates;1. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan;2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan;3. Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan;4. Department of Information Management, National University of Kaohsiung, Kaohsiung 811, Taiwan |
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| Abstract: | Neuro-oncologists must ultimately rely on their acquired knowledge and accumulated experience to undertake the sensitive task of brain tumour diagnosis. This task strongly depends on indirect, non-invasive measurements, which are the source of valuable data in the form of signals and images. Expert radiologists should benefit from their use as part of an at least partially automated computer-based medical decision support system. This paper focuses on Magnetic Resonance Spectroscopy signal analysis and illustrates a method that combines Gaussian Decomposition, dimensionality reduction by Moving Window with Variance Analysis and classification using adaptively regularized Artificial Neural Networks. The method yields encouraging results in the task of binary classification of human brain tumours, even for tumour types that have seldom been analyzed from this viewpoint. |
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| Keywords: | Brain tumour diagnosis Magnetic Resonance Spectroscopy Moving Window and Variance Analysis Bayesian Neural Networks |
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