Prediction of MRI Hardware Failures based on Image Features using Ensemble Learning

Kuhnert N, Pflueger L, Maier A (2020)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2020

Event location: Berlin DE

DOI: 10.1007/978-3-658-29267-6_28

Abstract

In order to ensure trouble-free operation, prediction of hardware failures is essential. This applies especially to medical systems. Our goal is to determine hardware which needs to be exchanged before failing. In this work, we focus on predicting failures of head/neck coils using image-related measurements. Thus, we aim to solve a classification problem with two classes, normal and broken coil. To solve this problem, we use data of two different levels. One level refers to one-dimensional features per individual coil channel on which we found a fully connected neural network to perform best. Furthermore, we use matrix features representing the state of an entire coil to train another neural network on. This ensemble of two networks and combining them using a Random Forest improves the prediction results even further. Thus, combining insights of both trained models allows us to determine the coil's condition with an F-score of 94.14% and an accuracy of 99.09%.

Authors with CRIS profile

Nadine Rücker Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

How to cite

APA:

Kuhnert, N., Pflueger, L., & Maier, A. (2020). Prediction of MRI Hardware Failures based on Image Features using Ensemble Learning. In Proceedings of the Bildverarbeitung für die Medizin 2020. Berlin, DE.

MLA:

Kuhnert, Nadine, Lea Pflueger, and Andreas Maier. "Prediction of MRI Hardware Failures based on Image Features using Ensemble Learning." Proceedings of the Bildverarbeitung für die Medizin 2020, Berlin 2020.

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