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Performance Improvement with Optimization Algorithm in Isolating Left Ventricle and Non-Left Ventricle Cardiac

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Abstract

Magnetic Resonance Imaging (MRI) typically shows the overall heart anatomy and usually includes the outmost slices of the left ventricle coverage. In assessing the patient in the left ventricle (LV) cardiac segment, only slices with images of the LV cardiac segment are considered, and the rest is neglected. This chapter explores an automated approach to classifying LV and Non-LV segments in cardiac MR images by utilizing a deep convolutional neural network. The dataset used is the STACOM2012 public dataset, which consists of 398 short-axis images of cardiac LGE-MRI. A deep convolution network model designed from scratch and three deep transfer learning models (AlexNet, GoogleNet and SqueezeNet) were trained on 80% of the images and validated on the remaining 20% of the images after the data augmentation process for a comparative analysis using three different optimization algorithms (ADAM, SGDM and RMSprop). Then, all networks were tested on cardiac LGE-MRI collected from Advanced Medical and Dental Institute (AMDI) USM database. The outcome demonstrated that Adam was the best network optimizer, with an accuracy improvement of 0.3–1.1% over SGDM. The GoogleNet model outperformed other models with an accuracy performance of 97.54% and a macro F1-score of 0.9080 when tested with the STACOM2012 and AMDI datasets, respectively.

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Acknowledgements

This research work ethics approval is obtained from Universiti Sains Malaysia (USM/JEPeM/21090623). The authors would like to express their gratitude to members of the Advanced Control System and Computing Research Group (ACSCRG), Advanced Rehabilitation Engineering in Diagnostic and Monitoring Research Group (AREDiM), Integrative Pharmacogenomics Institute (iPROMISE) and Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang for their assistance and guidance during the fieldwork. Finally, the authors are grateful to Universiti Teknologi MARA, Cawangan Pulau Pinang for their immense administrative and financial support.

Author information

Authors and Affiliations

  1. Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Penang, Malaysia

    Dayang Suhaida Awang Damit, Muhammad Khusairi Osman & Belinda Chong Chiew Meng

  2. Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Penang, Malaysia

    Siti Noraini Sulaiman

  3. Integrative Pharmacogenomics Institute (iPROMISE), UiTM Puncak Alam Campus, Bandar Puncak Alam, Selangor, Malaysia

    Siti Noraini Sulaiman

  4. Department of Biomedical Imaging, Advanced Medical & Dental Institute, Universiti Sains Malaysia, Kepala Batas, Pulau Pinang, Malaysia

    Noor Khairiah A. Karim

  5. Heart Failure Research Initiative, Advanced Medical & Dental Institute, Universiti Sains Malaysia, Kepala Batas, Pulau Pinang, Malaysia

    Noor Khairiah A. Karim

Authors
  1. Dayang Suhaida Awang Damit
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  2. Siti Noraini Sulaiman
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  3. Muhammad Khusairi Osman
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  4. Noor Khairiah A. Karim
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  5. Belinda Chong Chiew Meng
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Correspondence to Siti Noraini Sulaiman .

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Editors and Affiliations

  1. Department of Electronics and Instrumentation, University of Kashmir, Srinagar, Jammu and Kashmir, India

    Shabir A. Parah

  2. Aligarh Muslim University, Aligarh, India

    Nasir N. Hurrah

  3. Department of Electronics Engineering, Aligarh Muslim University, AMU Aligarh, India

    Ekram Khan

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Damit, D.S.A., Sulaiman, S.N., Osman, M.K., Karim, N.K.A., Meng, B.C.C. (2023). Performance Improvement with Optimization Algorithm in Isolating Left Ventricle and Non-Left Ventricle Cardiac. In: Parah, S.A., Hurrah, N.N., Khan, E. (eds) Intelligent Multimedia Signal Processing for Smart Ecosystems. Springer, Cham. https://doi.org/10.1007/978-3-031-34873-0_8

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  • DOI: https://doi.org/10.1007/978-3-031-34873-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34872-3

  • Online ISBN: 978-3-031-34873-0

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