Publication type: Conference paper
Type of review: Peer review (abstract)
Title: Influence of input image configurations on output of a convolutional neural network to detect cerebral aneurysms
Authors: Watanabe, Kazuhiro
Anzai, Hitomi
Juchler, Norman
Hirsch, Sven
Ohta, Makoto
et. al: No
DOI: 10.1115/IMECE2019-11125
Proceedings: International Mechanical Engineering Congress and Exposition : Volume 3 - biomedical and biotechnology engineering
Volume(Issue): 3
Conference details: 2019 International Mechanical Engineering Congress and Exposition, IMECE2019, Salt Lake City, Utah, USA, 11-14 November 2019
Issue Date: 21-Jan-2020
Publisher / Ed. Institution: The American Society of Mechanical Engineers
ISBN: 978-0-7918-5940-7
Language: English
Subjects: Intracranial aneurysms; Convolutional neural network; Computer assisted detection
Subject (DDC): 006: Special computer methods
610: Medicine and health
Abstract: Rupture of cerebral aneurysms is the main cause of subarachnoid hemorrhage, which can have devastating effects on quality of life. The identification and assessment of unruptured aneurysms from medical images is therefore of significant clinical relevance. In recent years, the availability of clinical imaging data has rapidly increased, which calls for computer assisted detection (CAD) systems. Previous studies have shown that CAD systems based on convolutional neural networks (CNN) can help to detect cerebral aneurysms from magnetic resonance angiographies (MRAs). However, these CAD systems require large datasets of annotated medical images. Thus, more efficient tools for processing and categorizing medical imaging data are required. Previous studies of CNN-based classification for medical images used various patch configurations of input data. These studies showed that classification accuracy was affected by the patch size or image representation. Thus, we hypothesize that the accuracy of CADs to detect cerebral aneurysms can be improved by adjusting the configuration of the input patches. In the present study, we performed CNN-based medical imaging classification for varying input data configurations to examine the relationship between classification accuracy and data configuration.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Published as part of the ZHAW project: AneuX
Appears in collections:Publikationen Life Sciences und Facility Management

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