Improved method for COVID-19 classification of complex-architecture CNN from chest CT volumes using orthogonal ensemble networks

Ryo Toda; Masahiro Oda; Yuichiro Hayashi; Yoshito Otake; Masahiro Hashimoto; Toshiaki Akashi; Shigeki Aoki; Kensaku Mori

doi:10.1117/12.2653792

7 April 2023 Improved method for COVID-19 classification of complex-architecture CNN from chest CT volumes using orthogonal ensemble networks

Ryo Toda, Masahiro Oda, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori

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Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124650D (2023) https://doi.org/10.1117/12.2653792
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

This paper introduces the improved method for the COVID-19 classification based on computed tomography (CT) volumes using a combination of a complex-architecture convolutional neural network (CNN) and orthogonal ensemble networks (OEN). The novel coronavirus disease reported in 2019 (COVID-19) is still spreading worldwide. Early and accurate diagnosis of COVID-19 is required in such a situation, and the CT scan is an essential examination. Various computer-aided diagnosis (CAD) methods have been developed to assist and accelerate doctors’ diagnoses. Although one of the effective methods is ensemble learning, existing methods combine some major models which do not specialize in COVID-19. In this study, we attempted to improve the performance of a CNN for the COVID-19 classification based on chest CT volumes. The CNN model specializes in feature extraction from anisotropic chest CT volumes. We adopt the OEN, an ensemble learning method considering inter-model diversity, to boost its feature extraction ability. For the experiment, We used chest CT volumes of 1283 cases acquired in multiple medical institutions in Japan. The classification result on 257 test cases indicated that the combination could improve the classification performance.

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Ryo Toda, Masahiro Oda, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, and Kensaku Mori "Improved method for COVID-19 classification of complex-architecture CNN from chest CT volumes using orthogonal ensemble networks", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124650D (7 April 2023); https://doi.org/10.1117/12.2653792

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