A4 Refereed article in a conference publication

Comparison of 12 Machine Learning Methods for Polar Map Classification in Cardiac Perfusion PET




AuthorsTeuho Jarmo, Schultz Jussi, Klén Riku, Saraste Antti, Ono Naoaki, Kanaya Shigehiko

EditorsTomita Hideki, Nakamura Tatsuya

Conference nameIEEE Nuclear Science Symposium and Medical Imaging Conference

PublisherInstitute of Electrical and Electronics Engineers Inc.

Publication year2021

JournalIEEE Nuclear Science Symposium and Medical Imaging Conference record

Book title 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors, RTSD 2022

Journal name in source2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors, RTSD 2022

Series titleIEEE Nuclear Science Symposium and Medical Imaging Conference record

ISBN978-1-66542-113-3

eISBN978-1-6654-2113-3

ISSN1091-0026

DOIhttps://doi.org/10.1109/NSS/MIC44867.2021.9875597(external)

Web address https://ieeexplore.ieee.org/document/9875597(external)


Abstract

We evaluated 12 open source machine learning methods for classification of polar map images in cardiac perfusion positron emission tomography (PET) using a dataset consisting of 138 polar maps. Majority of the classifiers showed good accuracy in 10-fold cross-validation (mean accuracy of 0.75–0.88). Accuracy was slightly lower when applied to a separate hold-out dataset (0.70-0.87). From the evaluated classifiers, a support vector machine using a polynomial kernel, Gaussian naive Bayes classifier and a two-dimensional convolutional neural network had stable performance across both the cross-validation and hold-out datasets (accuracy of 0.78, 0.83 and 0.87).



Last updated on 2024-26-11 at 22:56