Decalcify cardiac CT: unveiling clearer images with deep convolutional neural networks
Decalcification is crucial in enhancing the diagnostic accuracy and interpretability of cardiac CT images, particularly in cardiovascular imaging. Calcification in the coronary arteries and cardiac structures can significantly impact the quality of the images and hinder precise diagnostics. This stu...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1475362/full |
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| author | Gopinath Nagarajan Anandh Rajasekaran Balaji Nagarajan Vishnu Kumar Kaliappan Abid Yahya Ravi Samikannu Ravi Samikannu Irfan Anjum Badruddin Sarfaraz Kamangar Mohamed Hussien |
| author_facet | Gopinath Nagarajan Anandh Rajasekaran Balaji Nagarajan Vishnu Kumar Kaliappan Abid Yahya Ravi Samikannu Ravi Samikannu Irfan Anjum Badruddin Sarfaraz Kamangar Mohamed Hussien |
| author_sort | Gopinath Nagarajan |
| collection | DOAJ |
| description | Decalcification is crucial in enhancing the diagnostic accuracy and interpretability of cardiac CT images, particularly in cardiovascular imaging. Calcification in the coronary arteries and cardiac structures can significantly impact the quality of the images and hinder precise diagnostics. This study introduces a novel approach, Hybrid Models for Decalcify Cardiac CT (HMDC), aimed at enhancing the clarity of cardiac CT images through effective decalcification. Decalcification is critical in medical imaging, especially in cardiac CT scans, where calcification can hinder accurate diagnostics. The proposed HMDC leverages advanced deep-learning techniques and traditional image-processing methods for efficient and robust decalcification. The experimental results demonstrate the superior performance of HMDC, achieving an outstanding accuracy of 97.22%, surpassing existing decalcification methods. The hybrid nature of the model harnesses the strengths of both deep learning and traditional approaches, leading to more transparent and more diagnostically valuable cardiac CT images. The study underscores the potential impact of HMDC in improving the precision and reliability of cardiac CT diagnostics, contributing to advancements in cardiovascular healthcare. This research introduces a cutting-edge solution for decalcifying cardiac CT images and sets the stage for further exploration and refinement of hybrid models in medical imaging applications. The implications of HMDC extend beyond decalcification, opening avenues for innovation and improvement in cardiac imaging modalities, ultimately benefiting patient care and diagnostic accuracy. |
| format | Article |
| id | doaj-art-b00ea8e0e4394bb281be643c36fad0af |
| institution | OA Journals |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-b00ea8e0e4394bb281be643c36fad0af2025-08-20T02:18:28ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-04-011210.3389/fmed.2025.14753621475362Decalcify cardiac CT: unveiling clearer images with deep convolutional neural networksGopinath Nagarajan0Anandh Rajasekaran1Balaji Nagarajan2Vishnu Kumar Kaliappan3Abid Yahya4Ravi Samikannu5Ravi Samikannu6Irfan Anjum Badruddin7Sarfaraz Kamangar8Mohamed Hussien9SRM Institute of Science and Technology, Chennai, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, IndiaDepartment of Electrical and Communications Systems Engineering, Botswana International University of Science and Technology, Palapye, BotswanaDepartment of Electrical and Communications Systems Engineering, Botswana International University of Science and Technology, Palapye, BotswanaDepartment of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, IndiaDepartment of Mechanical Engineering, College of Engineering, King Khalid University, Abha, Saudi ArabiaDepartment of Mechanical Engineering, College of Engineering, King Khalid University, Abha, Saudi ArabiaDepartment of Chemistry, Faculty of Science, King Khalid University, Abha, Saudi ArabiaDecalcification is crucial in enhancing the diagnostic accuracy and interpretability of cardiac CT images, particularly in cardiovascular imaging. Calcification in the coronary arteries and cardiac structures can significantly impact the quality of the images and hinder precise diagnostics. This study introduces a novel approach, Hybrid Models for Decalcify Cardiac CT (HMDC), aimed at enhancing the clarity of cardiac CT images through effective decalcification. Decalcification is critical in medical imaging, especially in cardiac CT scans, where calcification can hinder accurate diagnostics. The proposed HMDC leverages advanced deep-learning techniques and traditional image-processing methods for efficient and robust decalcification. The experimental results demonstrate the superior performance of HMDC, achieving an outstanding accuracy of 97.22%, surpassing existing decalcification methods. The hybrid nature of the model harnesses the strengths of both deep learning and traditional approaches, leading to more transparent and more diagnostically valuable cardiac CT images. The study underscores the potential impact of HMDC in improving the precision and reliability of cardiac CT diagnostics, contributing to advancements in cardiovascular healthcare. This research introduces a cutting-edge solution for decalcifying cardiac CT images and sets the stage for further exploration and refinement of hybrid models in medical imaging applications. The implications of HMDC extend beyond decalcification, opening avenues for innovation and improvement in cardiac imaging modalities, ultimately benefiting patient care and diagnostic accuracy.https://www.frontiersin.org/articles/10.3389/fmed.2025.1475362/fulldeep learningmachine learningcardiac classificationdecalcificationdeep convolutional neural networks (CNN)DenseNet |
| spellingShingle | Gopinath Nagarajan Anandh Rajasekaran Balaji Nagarajan Vishnu Kumar Kaliappan Abid Yahya Ravi Samikannu Ravi Samikannu Irfan Anjum Badruddin Sarfaraz Kamangar Mohamed Hussien Decalcify cardiac CT: unveiling clearer images with deep convolutional neural networks Frontiers in Medicine deep learning machine learning cardiac classification decalcification deep convolutional neural networks (CNN) DenseNet |
| title | Decalcify cardiac CT: unveiling clearer images with deep convolutional neural networks |
| title_full | Decalcify cardiac CT: unveiling clearer images with deep convolutional neural networks |
| title_fullStr | Decalcify cardiac CT: unveiling clearer images with deep convolutional neural networks |
| title_full_unstemmed | Decalcify cardiac CT: unveiling clearer images with deep convolutional neural networks |
| title_short | Decalcify cardiac CT: unveiling clearer images with deep convolutional neural networks |
| title_sort | decalcify cardiac ct unveiling clearer images with deep convolutional neural networks |
| topic | deep learning machine learning cardiac classification decalcification deep convolutional neural networks (CNN) DenseNet |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1475362/full |
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