Exploring advanced deep learning approaches in cardiac image analysis: A comprehensive review.

Journal: Computers in biology and medicine
Published Date: Jul 4, 2025

Abstract

BACKGROUND: Cardiac image analysis plays an important role in detecting and categorizing cardiovascular diseases (CVDs), such as coronary artery disease (CAD), heart failure, congenital heart defects, arrhythmias (irregular heartbeat), and valvular heart disease. The automated identification of these diseases represents a significant advancement in achieving early diagnosis and mitigating disease exacerbations. While extant methodologies offer advanced means for the automatic segmentation and identification of cardiac structures and pathologies, recent strides in deep learning (DL) and modern imaging modalities within cardiology have introduced new opportunities for researchers. This has underscored the importance of deep model compression and optimization techniques. This review comprehensively surveys recent deep learning applications in interpreting cardiac images, encompassing common imaging modalities.

Authors

  • Assia Boukhamla
    LabGED Laboratory, Department of Computer Science, Faculty of Technology, Badji Mokhtar - Annaba University 12, P.O.Box, 23000, Annaba, Algeria. Electronic address: assia.boukhamla@univ-annaba.dz.
  • Nabiha Azizi
    LabGED Laboratory, Department of Computer Science, Faculty of Technology, Badji Mokhtar - Annaba University 12, P.O.Box, 23000, Annaba, Algeria. Electronic address: nabiha.azizi@univ-annaba.dz.
  • Samir Brahim Belhaouari
    Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University (HBKU), Doha, Qatar.
  • Nilanjan Dey
    Department of Information Technology, Techno India College of Technology, Kolkata, India.

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