Automatic Diagnosis of Myocarditis in Cardiac Magnetic Images Using CycleGAN and Deep PreTrained Models | SpringerLink
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Automatic Diagnosis of Myocarditis in Cardiac Magnetic Images Using CycleGAN and Deep PreTrained Models

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Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications (IWINAC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13258))

Abstract

Myocarditis is a cardiovascular disease caused by infectious agents, especially viruses. Compared to other cardiovascular diseases, myocarditis is very rare, occurring mainly due to chest pain or heart failure. Cardiac magnetic resonance (CMR) imaging is a popular technique for diagnosis of myocarditis. Factors such as low contrast, different noises, and high CMR slices of each patient cause many challenges when diagnosing myocarditis by specialist physicians. Therefore, it is necessary to introduce new artificial intelligence (AI) techniques for diagnosis of myocarditis from CMR images. This paper presents a new method to detect myocarditis in CMR images using deep learning (DL) models. First, the Z-Alizadeh Sani myocarditis dataset was used for simulations, which included CMR images of normal subjects and myocardial infarction patients. Next, preprocessing is performed on CMR images. CMR images are created with the help of the cycle generative adversarial network (GAN) model at this step. Finally, pretrained models including EfficientNet B3, EfficientNet V2, HrNet, ResNetrs50, ResNest50d, and ResNet 50d have been used to classify the input data. Among pretrained methods, the EfficientNet V2 model has achieved 99.33% accuracy.

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran

    Afshin Shoeibi

  2. Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran

    Navid Ghassemi

  3. Department of Mathematics and Computer Science, University of La Rioja, La Rioja, Spain

    Jonathan Heras

  4. Electrical and Computer Engineering Department, Tarbiat Modaters University, Tehran, Iran

    Mitra Rezaei

  5. Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Spain

    Juan M. Gorriz

Authors
  1. Afshin Shoeibi
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  2. Navid Ghassemi
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  3. Jonathan Heras
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  4. Mitra Rezaei
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  5. Juan M. Gorriz
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Corresponding author

Correspondence to Afshin Shoeibi .

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Editors and Affiliations

  1. Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Ramón Álvarez-Sánchez

  3. Universidad Nacional de Educación a Distancia, Madrid, Spain

    Félix de la Paz López

  4. Ohio State University, Columbus, OH, USA

    Hojjat Adeli

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Shoeibi, A., Ghassemi, N., Heras, J., Rezaei, M., Gorriz, J.M. (2022). Automatic Diagnosis of Myocarditis in Cardiac Magnetic Images Using CycleGAN and Deep PreTrained Models. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_15

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  • DOI: https://doi.org/10.1007/978-3-031-06242-1_15

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