Research Article Open Access

Cardiovascular Disease Prediction through Ensembled Transfer Learning on Cardiac Magnetic Resonance Imaging

Sibu Cyriac1, Sivakumar R.1 and Nidhin Raju2
  • 1 Department of Computer Science, CHRIST (Deemed to be University), India
  • 2 Centre for Digital Innovation, CHRIST (Deemed to be University), India

Abstract

Cardiovascular Diseases (CVD) cause more deaths worldwide than most of the other diseases. The diagnosis of cardiovascular disease from Magnetic Resonance Imaging plays a major role in the medical field. The technological revolution contributed a lot to increase the effectiveness of CVD diagnosis. Many Artificial Intelligence methods using Deep Learning models are available to assist the cardiologist in the diagnosis of CVD from Magnetic Resonance Imaging (MRI). In this study, we leverage on the merits of deep learning, transfer learning, and ensemble voting to improve the accuracy of Artificial Intelligence-based CVD detection. VGG16, MobileNetV2, and InceptionV3, trained on ImageNet, are the models used and the dataset is the Automatic Cardiac Diagnosis Challenge dataset. We customized the classification layers of all three models to suit the CVD detection problem. The results from these models are ensembled using the soft-voting and hard-voting approaches. Test accuracies obtained are 97.94% and 98.08% from hard-voting and soft-voting respectively. The experimental results demonstrated that the ensemble of outputs from transfer learning-based Deep Learning models produces much improved results for CVD diagnosis from MRI images.

Journal of Computer Science
Volume 18 No. 11, 2022, 1005-1020

DOI: https://doi.org/10.3844/jcssp.2022.1005.1020

Submitted On: 15 July 2022 Published On: 2 November 2022

How to Cite: Cyriac, S., R., S. & Raju, N. (2022). Cardiovascular Disease Prediction through Ensembled Transfer Learning on Cardiac Magnetic Resonance Imaging. Journal of Computer Science, 18(11), 1005-1020. https://doi.org/10.3844/jcssp.2022.1005.1020

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Keywords

  • Cardiovascular Disease
  • Deep Learning
  • Medical Imaging
  • ACDC Dataset
  • Transfer Learning